Educational Resources for Radar Remote Sensing

Introduction Introductory Comments Technical Notes

Curriculum Outlines

Course 1 Course 2

Basic Introduction to RADAR Remote Sensing

Notes RADARSAT-1

Notes RADARSAT-2 RADAR Systems

Notes Intermediate

SAR Image Formation SAR Image Characteristics Data Products Image Quality and Calibration Radiometric Enhancement Geometric Characteristics Classification & Information Extraction (Image Exploitation)

Advanced

Radar Systems and Digital Signal Processing Notes

Polarimetry Notes

Interferometry Notes

Applications Land Applications Agriculture Forestry Geology Hydrology Land Use and Land Cover Mapping Oceans Sea Ice SAR Interferometry

Bibliography

Glossary Acronyms Acknowledgements

Educational Resources for Radar Remote Sensing

Table of Contents

Introduction

Welcome to the GlobeSAR-2 Radar Remote Sensing Training package, a comprehensive and unique set of radar remote sensing training materials. Our goal is to make these materials available for use by universities and for general educational purposes around the world. To reach a wider audience, the material has been produced in four languages; English, French, Spanish, and Portuguese.

This CD-ROM was produced as part of GlobeSAR-2 Program to support the development of radar training capabilities in universities and agencies in South and Central America. It incorporates training slides developed by scientists at the Canada Centre for Remote Sensing for international technical co-operation programs, including GlobeSAR and ProRadar. Significant contributions have also been made by radar specialists from different disciplines and by scientists and user agencies inmany countries, particularly in South and Central America.

The slides have been divided into four main sections: basic, intermediate, advanced, and applications. Each section includes theory and image examples, with associated explanations.

The intent of this package is to provide a ‘toolkit’ of instructional materials that may be customized to suit the needs of each instructor and audience. It is expected that users will pick-and-choose the material most appropriate to the background and technical level of the audience. The material was developed primarily for audiences interested in the geoscience applications of radar imagery, but the ‘Advanced Radar Techniques’ section will be of relevance to the engineering and signal processing disciplines.

Permission for Use

Educators are encouraged to use the material for their own teaching needs, but it must be clearly indicated that the Canada Centre for Remote Sensing is the originator of this material and appropriate credit must to given to the authors at all times. These documents may be reproduced in whole, for training and educational purposes, but not for commercial exploitation. CCRS reservesthe right of distribution of this material. Requests for further copies may be directed to the Canada Centre for Remote Sensing GlobeSAR Program. GlobeSAR Program Canada Centre for Remote Sensing Natural Resources Canada 588 Booth Street Ottawa, Ontario K1A 0Y7 CANADA E-mail: [email protected] WWW: http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/gsarmain_e.html

Page 1 of 1Introduction

http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/gsarmain_e.html


http://www.ccrs.nrcan.gc.ca/ccrsnew/learn/tutorials/gsarCD/credit_e.html

Technical Notes

To run this product, you require the following minimum configuration:

486™ or Pentium® processor-based personal computer Microsoft® Windows 95 or later 10 MB of available hard-disk space (for installation of Acrobat Reader) Netscape 4x or Internet Explorer 4x or above with JavaScript and Java enabled You must use Adobe Acrobat Reader Version 4.0 or later with Web Browser Integration of the PDF viewer. It is available free of charge from the Adobe Web site (http://www.adobe.com/).

A resolution of 800 x 600 or higher Colour depth of 16 bit or higher

Page 1 of 1Technical Notes

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RADAR Remote Sensing Course Curriculum Outline

The following is meant as an outline for a two-course curriculum aimed at senior undergraduates,graduate students, and application scientists. It is assumed that the participant has had apreliminary course on remote sensing, including an introduction to radar, or an equivalentexposure to basic concepts through work experience. The courses cover the physics, engineering,and target interaction concepts needed to work with radar data at an advanced level for geoscienceapplications. These course outlines can be modified to either expand or contract the material inorder to deliver short courses (i.e. days to weeks in duration) or full length university courses (i.e.approximately 12 weeks). Much of the material is covered in the GlobeSAR Level 1 and 2workshops with this outline expanding on some theoretical concepts and adding some material inorder to expand the curriculum for a university level course.

Course 1 - RADAR Physics and Engineering

1.1) Wave Fundamentals

Phase, Amplitude and Wavelength electric and magnetic fields electromagnetic wave equation

Polarization and Radar Conventions Microwave spectrum and band assignments Propagation of EM Radiation

in free space in isotropic dielectrics

lossless media lossy media (skin depth, absorption, & extinction)

in anisotropic dielectrics reciprocal media optically active media (ionospheric propagation) superposition theorems and implications wave interference

Ensemble Concepts degree of polarization coherence, partial coherence, incoherent radiation

Antenna Concepts physical principles antenna gain near and far fields antenna efficiency antenna polarization antenna pattern (main lobe, sidelobes, polarization dependence, effects of aperture weighting, arrays and phase steering) polarization isolation

Page 1 of 3Curriculum Outlines - Course 1

1.2) Scattering

Dielectric Constant polarized vs unpolarized materials in the microwave regime (resonances, dielectric constant of water, dielectric constant of minerals) displacement vector, displacement currents

Boundary Conditions at a Dielectric Interface Reflection,Transmission, Refraction at a Boundary

Fresnel reflection coefficients refraction in graded dielectric materials reflections and transmission in layered media

Wave Interactions with Electrically Small Objects Rayleigh scattering Mie scattering edge diffraction geometric scattering limits Greens function concepts, vector potentials effects of scatterer shape as a function of scale size spatial distribution of scattered radiation forward scattering versus backscattering

Ensemble Concepts surface scattering

correlation lengths and roughness the role of surface penetration multiple reflections

volume scattering scale size distribution effects multiple scattering coherent versus incoherent models

Simple Scattering Models scattering matrix, scattering cross section, penetration depth, extinction coefficient

1.3) Radar Principles and Synthetic Aperture Radar

The Role of Time Pulse Compression The Sampling Theorem Radar Measurement Coordinates Coherence The Synthetic Aperture and Phase Histories The Frequency Coded Real Aperture

Matched Filter Concepts Pulse Compression and Focusing Concepts Impulse Response and Radar Resolution Differences Between Sample Spacing and Resolution The Radar Equation

detailed presentation of components making up the radar equation for monostaticradars implications for radar calibration terrain effects (materials, geometry, terrain relief)


real aperture vs synthetic aperture radar SAR Signal-to-Noise Ratio Equation Range and Azimuth Ambiguities and their Relationship to the Radar Equation

1.4) Signal, Noise, and Speckle

Physical Noise Sources types of noise (thermal, quantum, shot and flicker) noise statistics noise equations (brief-provide understanding of noise)

Misplaced Signals as Noise (scene dependent "noise") sampling noise range and azimuth ambiguities integrated sidelobe ratio peak sidelobe ratio coherent fading of random targets (wave interference phenomena)

Physics of Speckle (differentiate speckle from noise) Information Content of Speckle (tone, speckle, texture) Speckle Reduction

1.5) SAR/Signal Processing

Properties of SAR Phase History Range/Azimuth Coordinates vs Sampled Data Coordinates

range cell migration and range walk concepts squinted SAR

I/Q, Phase and Magnitude Representations of Complex Signals Measuring, Recording, and Calibrating Phase

Brief Overview of Waveform Generation, Frequency Modulation and Pulse Compression (CHIRP)

SAR processors Radar Equation Inversion for Calibration

generalized SAR block diagram SAR system parameters and implications SAR calibration calibration sources (internal, reference targets)

Slant/Ground Range Conversions with and without DEMs motion effects relief distortions (foreshortening, layover, & shadow) local incident angle effects slant range to ground range projection on smooth surfaces slant range to ground range projection with DEM

Radar Signal Resampling and Image Degradation Registration of Coherent, Time Interleaved Channels Coherence Preservation in Processing

1.6) Advanced Topics

Interferometry Polarimetry Future SAR's


Course 2 - Target Interaction and Image Processing

2.1) Overview of System and Target Parameters

System and Target Parameter Descriptions system: frequency, polarization, incident angle, and resolution target: geometrical and dielectrical properties

Effects of System Parameters on Target Interactions frequency

dielectric constant of water versus frequency size/shape versus frequency penetration depth versus frequency Bragg scale roughness versus frequency

polarization polarized, unpolarized, and depolarized backscatter effects of target orientation

incident angle target (terrain, open water, sea ice) roughness versus range fall-off incident angle effects on Bragg scale roughness incident angle effects on information content

resolution resolution versus swath coverage and number of looks resolution and effects on information content

2.2) Dielectric Properties

Real and Imaginary Parts of Complex Dielectric Constant (CDC) effects of oscillating dipole and readmission of EM waves backscattering effects attenuation effects measuring CDC

Impacts of CDC on SAR Response CDC versus soil moisture CDC versus plant moisture CDC versus rock type CDC versus sea ice type Freeze/Thaw and environmental effects on CDC

2.3) Geometric Properties

Terrain Effects on Radar Backscatter review of image geometry layover, shadow, foreshortening artifacts local versus nominal incident angle look direction effects

Surface Roughness specular, slightly rough, and rough scattering patterns description of surface roughness parameters

Standard Deviation of Surface Height Correlation Length Periodic Surfaces


smooth surface criteria Rayleigh Criterion Fraunhofer Criterion

look direction effects Surface Scattering Models

physical optics, small perturbation, and geometric optics models model developments (layered dielectric and volume scattering)

Target Geometry the nature of volume scattering for land measuring vegetation geometry modelling vegetation components vegetation models

cloud model layered models multi-component/multi-constituent models model developments (vegetation dielectrics and geometry)

the nature of volume scattering for sea ice measuring sea ice structure and features

2.4) Radiometric Corrections to SAR Data

Beta Naught, Gamma Naught, and Sigma Naught Descriptions Calibration of SAR Data

relative versus absolute calibration antennae pattern determination and correction range dependent gain corrections absolute calibration via point targets Earth terrain model implications

Radiometric Enhancements speckle reduction

non-adaptive filters description and advantages (FFT filters) adaptive filters description and advantages (Frost, Lee, MAP Gamma)

edge detection ratio edge detection filter Touzi filter

analysis of image texture visual enhancement

contrast enhancement linear enhancement non-linear enhancements

2.5) Geometric Correction and Exploitation of SAR Data

SAR Platform - Target Geometry image acquistion relief displacement

layover foreshortening shadow

radiometric distortion local incident angle effects


effects of geometry on image brightness Geometric Correction Methods

principle of SAR geocoding slant to ground range conversion image registration polynomial transformations radargrammetric method and advantages planimetric accuracy of an ortho-image

as a function of DEM and incident angles sources of errors in the ortho-image

comparison of methods Image Resampling Algorithms

bilinear interpolation cubic convolution sinx / x

Radar Stereoscopy: Approach, Advantages, and Applications radar versus optical stereoscopy compromises between geometry and radiometry and their consequences selection of stereo configurations

radiometric and geometric disparities parallax - same side versus opposite side viewing

guidelines for DEM extraction (same side versus opposite) Interferometry

satellite interferometry (repeat versus single pass) geometry for repeat pass interferometry InSAR processing terrain height applications

approach and examples conditions required accuracy of terrain height measurements

velocity applications approach and examples conditions required accuracy of velocity measurements

2.6) Information Extraction from SAR Data

Overview of General Information Extraction Methods classification techniques

supervised and unsupervised classification classification algorithms accuracy assessment new classification approaches

change detection difference image ratio image classification comparison change vector analyses

data presentation and integration RGB colour space IHS colour space Principal Components Analysis


2.7) Environmental Effects on SAR Data

rain and dew effects snow and ice effects wind and wave effects change of state


Canada Centre for Remote Sensing, Natural Resources CanadaNatural Resources Ressources naturellesCanada Canada

Introduction to RADARRemote Sensing

Canada Centre for Remote Sensing, Natural Resources Canada

Course Outline

• Why use RADAR for Remote Sensing?• Fundamentals of RADAR

– SAR– Resolution and incident angle– Frequency and Polarization

• Image Characteristics– Topographic Displacement– Speckle

• Scattering Mechanisms• Introduction to Sensors


Why Use Radar for Remote Sensing?• Controllable source of illumination

– sees through cloud and rain, and at night• Images can be high resolution (3 - 10 m)• Different features are portrayed or discriminated

compared to visible sensors• Some surface features can be seen better in

radar images:– ice, ocean waves– soil moisture, vegetation mass– man-made objects, e.g. buildings– geological structures


Radar is an acronym for Radio Detection And Ranging.A Radar system has three primary functions:- It transmits microwave (radio) signals towards a scene- It receives the portion of the transmitted energy backscattered from the scene- It observes the strength (detection) and the time delay (ranging) of the return signals.

Radar provides its own energy source and, therefore, can operate both day or night and through cloud cover. This type of system is known as an active remote sensing system.

RADAR


RADAR - Radio Detection And Ranging

Pulse

Range

Echo


All electromagnetic waves propogate at the speed of light. X-rays, visible light, and radio waves are some examples. Such waves are described by variations in their electric and magnetic fields.

Electromagnetic waves are characterized by polarization, and by frequency or wavelength (inversely proportional to frequency).

Radar remote sensing uses the microwave portion of the electromagnetic spectrum, from a frequency of 0.3 GHz to 300 GHz, or in wavelength terms, from 1 m to 1 mm.

The Electromagnetic Spectrum


Electromagnetic Spectrum


What is Synthetic Aperture Radar (SAR)?

• A side-looking radar system which makes a high-resolution image of the Earth’s surface (for remote sensing applications)

• As an imaging side-looking radar moves along its path, it accumulates data. In thisway, continuous strips of the ground surface are “illuminated” parallel and to one side of the flight direction. From this record of signal data, processing is needed to produce radar images.

• The across-track dimension is referred to as “range”. Near range edge is closest to nadir (the points directly below the radar) and far range edge is farthest from the radar.

• The along-track dimension is referred to as “azimuth”.

• In a radar system, resolution is defined for both the range and azimuth directions.

• Digital signal processing is used to focus the image and obtain a higher resolution than achieved by conventional radar


Concept of Synthetic ApertureSynthetic Aperture

Swath

Nadir

First time SARsenses object

Last time SARSenses object

Flight Path

Ground Track

Distance SAR travelled while objectwas in view = synthetic aperture

Object


Since SAR is an active system, the actual sensor resolution has two dimensions: range resolution and azimuth resolution. Resolution of a SAR sensor should not be confused with pixel spacing which results from sampling done by the SAR image processor.

RangeRange resolution of a SAR is determined by built-in radar and processor constraints which act in the slant range domain. Range resolution is dependent on the length of the processed pulse; shorter pulses result in “higher” resolution. Radar data are created in the slant range domain, but usually are projected onto the ground range plane when processed into an image.

AzimuthFor a real aperture radar, azimuth resolution is determined by the angular beam width of the terrain strip illuminated by the radar beam. For two objects to be resolved, they must be separated in the azimuth direction by a distance greater than the beam width on the ground. SAR gets its name from the azimuth processing and can achieve an azimuth resolution which may be hundreds of times smaller than the transmitted antenna beam width.

Resolution


originalazimuthbeamwidthProcessed azimuth

resolution

Azimuth Resolution

A simple (i.e. real-aperture) radar has an azimuth resolution given by the azimuth beam width

A synthetic aperture radar (SAR) uses signal processing to refine the azimuth resolution to shorter than the antenna length


Resolution Cell

rR = range resolution rA = azimuth resolution

Source: Raney, 1998


Incident Angle

Refers to the angle between the radar illumination and the normal to the ground surface. Depending on the height of the radar above the Earth’s surface, the incident angle will change from the near range to the far range which in turn affects the viewing geometry.

Local Incident Angle

The term local incident angle takes into account the local slope of the terrain at any location within the image.

It is the local incident angle which in part determines the image brightness or tone for each pixel.


Most remote sensing radars operate at wavelengths between .5 cm to 75 cm. The microwave frequencies have been arbitrarily assigned to bands identified by letter. The most popular of these bands for use by imaging radars include:

X-band: from 2.4 to 3.75 cm (12.5 to 8 GHz). Widely used for military reconnaissance and commercially for terrain surveys. Used on CV-580 SAR (Environment Canada).

C-band: from 3.75 to 7.5 cm (8 to 4 GHz). Used in many spaceborne SARs, such as ERS-1 and RADARSAT.

S-band: from 7.5 to 15 cm (4 to 2 GHz). Used in Almaz.

L-band: from 15 to 30 cm (2 to 1 GHz). Used on SEASAT and JERS-1.

P-band: from 30 to 100 cm (1 to 0.3 GHz). Used on NASA/JPL AIRSAR.

The capability to penetrate through precipitation or into a surface layer is increased with longer wavelengths. Radars operating at wavelengths greater than 2 cm are not significantly affected by cloud cover, however, rain does become a factor at wavelengths shorter than 4 cm.

Microwaves


Relative Size of Microwave Wavelengths


Choice of Radar Frequency 1

• Application factors:

– Radar wavelength should be matched to the size of the surface features that we wish to discriminate

– e.g. Ice discrimination, small features, use X-band

– e.g. Geology mapping, large features, use L-band

– e.g. Foliage penetration, better at low frequencies, use P-band

In general, C-band is a good compromise


Frequency Comparison: C-, L-, and P-Bands

FREQUENCY COMPARISONFlevoland, Netherlands Agricultural Scene

L-Band P-Band

C-Band

Multipolarizationcolour composites courtesy of JPL


Choice of Radar Frequency 2• System factors:

– Low frequencies:More difficult processingNeed larger antennas and feedsSimpler electronics

– High frequencies:Need more powerMore difficult electronicsGood component availability at X-band

• Note that many research SARs have multiple frequency bands – e.g. JPL AIRSAR, SIR-C, Convair-580


Polarization refers to the orientation of the electric vector of an electromagnetic wave.

Radar system antennas can be configured to transmit and receive either horizontally or vertically polarized electromagnetic radiation.

When polarization of the transmitted and received waves is in the same direction, it is referred to as like-polarized. HH refers to horizontally transmitted and received waves; VV refers to vertically transmitted and received waves.

When polarization of the transmitted waves is orthogonal to the polarization of the received radiation, it is referred to as cross-polarized; e.g. HV refers to horizontal transmission and vertical reception; VH for vertical transmission and horizontal reception.

When the radar wave interacts with a surface and is scattered from it, the polarization can be modified, depending upon the properties of the surface. This modification affects the way the scene appears in polarimetric radar imagery, and the type of surface can often be deduced from the image.

Polarization


EM Wave PolarizationElectrical Field

HORIZONTAL POLARIZATION

VERTICAL POLARIZATION


Choice of Polarization

• Basic or operational SARs usually have only one polarization for economy, e.g. HH or VV

• Research systems tend to have multiple polarizations, e.g. all of: HH, HV, VV, VH (quad pol)

• Multiple polarizations help to distinguish the physical structure of the scattering surfaces:– the alignment with respect to the radar (HH vs. VV)– the randomness of scattering (e.g. vegetation - HV)– the corner structures (e.g. HH VV phase angle)– Bragg scattering (e.g. oceans - VV)


Weddell Sea Ice, Antarctica

C-band, HH L-band, HV L-band, HH

Shuttle SIR-C/X Image


Victoria & Saanich Peninsula, Canada


Urban

Forest

Agriculture / Clear-cut

Suburban



Benefits of Polarimetry• the scattering matrix, Stokes matrix and polarization

signature can be computed for each pixel

– can be a powerful classification tool

– for both visual and machine classification

• the scattering matrix can be used

– to synthesize the return with any transmit/receive polarizations

– to investigate the scattering properties of different surfaces

– to optimize polarization for optimum detectability


Benefits of Multipolarimetric Imagery


Since imaging radars usually view the scene from an oblique perspective (i.e. Side-looking), they are subject to one-dimensional relief displacement analogous to that inherent in aerial photography.

Tall objects are displaced radially from nadir in air photos, whereas terrain distortion in radar imagery is perpendicular to the flight path (or satellite track) which results in tall objects being displaced toward the sensor.

Relief Displacement


Topographic Displacement - Optical SensorOptical Sensor

by similar triangles

reference surface

Topographic displacements Optical Sensor

nadir

θ

d = Horizontal displacement of a 100m mountain top(m)

H

d = hD H

D = * D

d = h tan θ

hH


Topographic Displacement - Radar Sensor

Source: T. Toutin, 1992, ROS and SEASAT Image Geometric Correction IEEE-IGARS, Vol. 30, No. 3, pp. 603-609.

θ

θ

apparentviewingdirection

mountain top

reference surface orthographicprojection ofmountaintop

airborne

satellite

Horizontal displacement of a 100m mountain top(m)

radar ground rangeprojection of mountaintop


Radar shadows in imagery indicate those areas on the ground surface not illuminated by the radar. Since no return signal is received, radar shadows appear very dark in tone on the imagery.

In imagery, radar shadows occur in the down-range direction behind tall objects. They are a good indicator of radar illumination direction if annotation is missing or incomplete.

Since incident angle increases from near to far-range, terrain illumination becomes more oblique. As a result, shadowing becomes more prominent toward far-range.

Information about the scene, such as an object’s height, can also be obtained from radar shadows. Shadowing in radar imagery is an important key for terrain relief interpretation.

Radar Shadow


Radar Shadow

Source: Raney, 1998

illumination

wave

front

distorsion shadow

scene


Foreshortening in a radar image is the appearance of compression of those features in the scene which are tilted toward the radar.

Foreshortening leads to relatively brighter appearance of these slopes, and must be accounted for by the interpreter.

Foreshortening is at a maximum when a steep slope is orthogonal to the radar beam. In this case, the local incident angle is zero, and as a result, the base, slope and top of a hill are imaged simultaneously and, therefore, occupy the same position in the image.

For a given slope or hillside, foreshortening effects are reduced with increasing incident angles. At the grazing angle, where incident angles approach 90°, foreshortening effects are eliminated, but severe shadowing may occur. In selecting incident angle, there is always a trade-off between the occurrence of foreshortening and the occurrence of shadowing in the image.

Foreshortening


Foreshortening

Source: Raney, 1998

scene

displacement

illumination

wavefr

ont

foreshortening


Layover occurs when the reflected energy from the upper portion of a feature is received before the return from the lower portion of the feature. In this case, the top of the feature will be displaced, or “laid over” relative to its base when it is processed into an image.

In general, layover is more prevalent for viewing geometries with small incident angles, such as from satellites.

Layover


Layover

llumination

distortion

wavefront

scene

layover

i

Source: Raney, 1998


Relief Displacement (Radar Sensor)

Local incident angle0° 90º

Layover Foreshortening Shadow

The type and degree of relief displacement in the radar image is a function of the angle at which the radar beam hits the ground, i.e. it depends upon the local slope of the ground.


Fading and speckle are the inherent “noise-like” processes which degrade image quality in a coherent imaging system.

Fading is due to variation in the echo phase delay caused by multiple targets in a resolution cell with range variations differing by less than a wavelength.

Local constructive and destructive interference appears in the image as bright and dark speckles, respectively.

Using independent data sets to estimate the same ground patch, by average independent samples, can effectively reduce the effects of fading and speckle. This can be done by:

• Multiple-look filtering, separates the maximum synthetic aperture into smaller sub-apertures generating independent looks at target areas based on the angular position of the targets. Therefore, looks are different Doppler frequency bands.

• Averaging (incoherently) adjacent pixels.

Reducing these effects enhances radiometric resolution at the expense of spatial resolution.

Fading and Speckle


SpeckleConstructive Interference

Destructive Interference

Result

Result

Example of Homogenous Target

Constructive interference

Destructive interference

Varying degrees of interference(between constructive and destructive )

Coherentradar waves


Corn Field Forest

300 m

Spatially Uniform TargetFine Texture

Spatially Non-Uniform TargetCoarse Texture

300 m

Speckle


Surface roughness influences the reflectivity of microwave energy and thus the brightness of features on the radar imagery.

Horizontal smooth surfaces reflect nearly all incident energy away from the radar and are called specular (from the Latin word speculum, meaning mirror). Specular surfaces, such as calm water or paved highways, appear dark on radar imagery.

Microwaves incident upon a rough surface are scattered in many directions. This is known as diffuse or distributed reflectance. Vegetation surfaces will cause diffuse reflectance,and result in a brighter tone on the radar imagery.

Diffuse and Specular Reflectance


Diffuse and Specular Reflectance

Diffuse Reflection Specular ReflectionCorner Reflector


In general, scenes observed by a SAR consist of two kinds of reflecting surfaces; distributed scatterers and discrete scatterers.

Discrete scatterers are characterized by a relatively simple geometric shape, such as a building. The classic element used to represent discrete scattering is a corner reflector, a shape as is formed when all sides intersect at (nearly) right angles (such as the intersection of a paved road and tall building).

Scatter 1


Distributed scatterers consist of multiple small areas or surfaces from which the incident microwaves scatter in many different directions. Distributed scattering is produced from a forest canopy or cultivated fields.

A radar measures that component of the scattered energy which returns along the same path of the incident beam.

Scatter 2


Surface roughness of a scattering surface is determined relative to radar wavelength and incident angle.

Generally, a surface is considered smooth if its height variations are considerably smaller than the radar wavelength. In terms of a single wavelength, a given surface appears rougher as incident angle increases.

Rough surfaces will usually appear brighter on radar imagery than smoother surfaces composed of the same material. In general a rough surface is defined as having a height variation of about half the radar wavelength.

Surface Roughness


Surface RoughnessSurface Scattering Patterns

Incident Wave Scattering Pattern

Smooth

Incident Wave Incident Wave

Very RoughMedium Rough

Scattering PatternScattering Pattern


Small objects may appear extremely bright on radar imagery. This is dependent on the geometric configuration of the object.

The side of a building or a bridge, combined with reflection from the ground is an example of a corner reflector.

When two surfaces are at right angles and open to the radar, a dihedral corner reflector is formed. The return from a dihedral corner reflector is strong only when the reflecting surfaces are very nearly perpendicular to the illumination direction.

Strong reflections are caused by a trihedral corner reflector. These are formed by the intersection of three mutually perpendicular plane surfaces open to the radar.

Researchers often place corner reflectors at various ground locations to act as reference points on the radar imagery.

Corner Reflectors


Corner Reflectors

Dihedral Trihedral


Volume scattering is related to multiple scattering processes within a medium, such as the vegetation canopy of a corn field or a forest. This type of scattering can also occur in layers of very dry soil, sand, or ice.

Volume scattering is important as it influences the backscatter observed by the radar. Radar will receive backscatter from both the surface and the volume.

The intensity of volume scattering depends on the physical properties of the volume (variations in dielectric constant, in particular) and the characteristics of the radar (wavelength, polarization and incident angle).

Volume Scattering


Reflections

Canopy Backscattering

SoilBackscattering

Soil - TrunkReflection

(Corner Reflector)

Canopy Soil Reflection


The presence of moisture increases a material’s complex dielectric constant. The dielectric constant influences the ability of a material to absorb, reflect and transmit microwave energy.

The moisture content of a material can change its electrical properties. This affects how a material appears on the radar image. Identical materials can vary in appearance at different times or different locations according to the amount of moisture they contain.

The reflectivity, and hence image brightness, of most natural vegetation and surfaces is increased with increasing moisture content.

Microwaves may penetrate very dry materials, such as desert sand. The scattering which results, is affected by both surface and subsurface properties. In general, the longer the radar wavelength, the deeper into the material the energy will penetrate.

Moisture Content


Comparison of Satellite SARs & Aircraft SARs• Advantages of satellite SARs

– More coverage per second (Km2/s)– Lower operating costs ($/Km2)– Not constrained by flying conditions or airport proximity– Wider area views– Somewhat simpler signal processing (no motion

compensation)• Disadvantages

– More expensive to design, build and launch– More difficult to provide multiple polarizations &

frequencies– Cannot be flown anywhere on demand– Lower resolution in general


airborne 10 – 100 kmspaceborne 25 – >500 km

IMAGE SWATH

SPACEBORNE SAR

AIRBORNE SAR

Comparison of Imaging Geometries


Choice of Swath Width

• Limited by range ambiguities and data handling capacity

• A trade-off between azimuth resolution, number of looks, processing capability

• For satellites: 30 - 150 Km typical• For aircraft: 10 - 100 Km typical• RADARSAT gets large swath widths per beam by

reducing the resolution, and using careful antenna weighting to control range ambiguities

• RADARSAT and the future Envisat use ScanSAR to get extra wide swaths


RADARSAT-1


FineStandard

WideScanSAR Extended High

Satellite Ground Track

Extended Low

RADARSAT-1 SAR Imaging Modes

Introduction To Radar Remote Sensing

Notes

Slide 35

This slide illustrates that shadow, foreshortening and layover are progressive forms of the same phenomenon — namely range-direction geometric distortion caused by the radar viewing geometry and the fact that the radar is basically a distance-measuring device ( a camera is an angle-measuring device).

You can also think of radar shadow and layover as extreme or terminal cases of foreshortening.

Slide 38

Speckle is the randomness of the observed reflectivity caused by the interference of multiple scatterers within a resolution cell, when the distance to the scattering centres of the reflectors is random. In general, only a pixel with a strong corner reflector does not exhibit speckle.

Pure speckle is observed in a radar image when the signal/noise ratio is high, and the true reflectivity of the ground is uniform.

However, speckle is usually accompanied in the radar image by other sources of noise and radiometric variation. These include random receiver noise, and true changes in the radar reflectivity across the scene.

The observed texture of the scene is a combination of the above factors. In general, scenes of areas with uniform reflectivity will exhibit fine texture, owing to the predominance of speckle. Scenes with varying reflectivity will exhibit coarser texture, as affected by the spatial distribution ofsurface reflectivity.

Page 1 of 1Introduction to Radar Remote Sensing Notes


RADARSAT-1


FineStandard

WideScanSAR Extended High

Satellite Ground Track

Extended Low

SAR Imaging Modes of RADARSAT-1



APPROXIMATE NOMINAL APPROXIMATE NUMBER OFMODE BEAM & INCIDENCE ANGLES GROUND a AREA PROCESSED

POSITION (DEGREES) RESOLUTION (M) (KM) LOOKSFine F1 near 36.4 - 39.6 8 50 X 50 1 X 1(15 positions) F1 36.8 - 39.9 SGF or SGXsee slide 5 F1 far 37.2 - 40.3

F2 near 38.8 - 41.8F2 near 39.2 - 42.1F2 far 39.6 - 42.5

F3 near 41.1 - 43.7F3 41.5 - 44.0

F3 far 41.8 - 44.3F4 near 43.1 - 45.5

F4 43.5 - 45.8F4 far 43.8 - 46.1

F5 near 45.0 - 47.2F5 45.3 - 47.5

F5 far 45.6 - 47.8Standard S1 20 - 27 25 100 x 100 1 x 4(7 beams) S2 24 - 31 SGF or SGX

S3 30 - 37S4 34 - 40S5 36 - 42S6 41 - 46S7 45 - 49

a Ground range resolution varies across the swath.

SGF = SAR Georeferenced Fine Resolution Product = Path ImageSGX = SAR Georeferenced Extra Fine Resolution Product = Path Image Plus



APPROXIMATE NOMINAL APPROXIMATE NUMBER OFMODE BEAM & INCIDENCE ANGLES GROUNDa AREA PROCESSED

POSITION (DEGREES) RESOLUTION (M) (KM) LOOKSWide W1 20 - 31 30 165 x 165 1 x 4(3 positions) W2 31 - 39 150 x 150 SGF or SGX

W3 39 - 45 130 x 130ScanSAR Narrow SCNA 20 - 40 50 300 x 300 2 x 2see slide 6 SCNB 31 - 46 SCNScanSAR Wide SCWA 20 - 49 100 500 x 500 2 x 4see slide 6 SCWB 20 - 46 450 x 450 SCWExtended High EH1 49 - 52 25 75 x 75 1 x 4(6 beams) EH2 50 - 53 SGF or SGX

EH3 52 - 55EH4 54 - 57EH5 56 - 58EH6 57 - 59

Extended Low EL1 10 - 23 30 170 x 170 1 x 4SGF or SGX

SGF = SAR Georeferenced Fine Resolution Product (Path Image)SGX = SAR Georeferenced Extra Fine Resolution Product (Path Image Plus)SCN = ScanSAR Narrow Beam Product (Path Image)SCW = ScanSAR Wide Beam Product (Path Image)



Image Position within a Fine Beam (F4)

RangeF4F

F4F4N

Two

Way

Ant

enna

Ele

vatio

n G

ain

[dB]


Combinations of Four Beams to Produce a ScanSAR Image (SCWA)

Range

Azim

uth

Wid

e 1

Wid

e 2

Wid

e 3

Stan

dard

7


RADARSAT-1 Image Products Sizes and Scales

MODE PROCESSING CEOS APPROXIMAGE DIGITAL FILM APPROX.LEVEL IMAGE DIGITAL FILE SIZE IMAGE SIZE FILM

PRODUCT IMAGE SIZE (MB) (CM)a SCALE(PIXELS x LINES)

Fine Path Image Plus SGX 16,000 x 16,000 512 N/A N/APath Image SGF 8,000 x 8,000 128 20 x 20 1:250,000Map Image SSG/SPG 8,000 x 8,000 64 20 x 20 1:312,500

Standard Path Image Plus SGX 12,500 x 12,500 313 N/A N/APath Image SGF 8,000 x 8,000 128 20 x 20 1:500,000Map Image SSG/SPG 8,000 x 8,000 64 20 x 20 1:625,000

Wide Path Image Plus SGX 15,000 x 15,000 450 N/A N/APath Image SGF 12,000 x 12,000 288 15 x 15b 1:250,000Map Image SSG/SPG 12,000 x 12,000 144 20 x 20c 1:625,000

ScanSAR Narrow Path Image SCN 12,000 x 12,000 144 15 x 15b 1:500,000ScanSAR Wide Path Image SCW 10,000 x 10,000 100 20 x 20 1:625,000Extended High Path Image Plus SGX 9,375 x 9,375 176 N/A N/A

Path Image SGF 6,000 x 6,000 72Map Image SSG/SPG 6,000 x 6,000 36

Extended Low Path Image Plus SGX 17,000 x 17,000 578 N/A N/APath Image SGF 13,600 x 13,600 370Map Image SSG/SPG 13,600 x 13,600 185

SPG products (Precision Map Image) have the same sizes and scales as SSG products (Map Image).a Film size is 24 x 24 cm.b The digital product is divided into quarters and imaged onto four 24 x 22 cm film transparencies.c The 8,000 x 8,000 line image is noted to be NORTH UP which requires approximately 40% additional image area.


RADARSAT-1 CoverageRADARSAT can provide complete global coverage with the flexibility to support specific requirements. The satellite's ground track is repeated every 24 days. RADARSAT can provide daily coverage of the Arctic, view any part of Canada within three days, and achieve complete coverage at equatorial latitudes every six days using a 500 kilometre wide swath.


This is the historical first image produced by RADARSAT-1 in November 1995.

After its launch on Nov 5, 1995, it completed its test and calibration phase on time, and has been performing within spec ever since.

Cape Breton Island, on Canada’s eastern coastline, can be seen surrounded by the Atlantic Ocean.


RADARSAT-1 Fine Mode: Singapore Harbour

Enlargement of central ship


RADARSAT-1 ScanSAR Wide: Labrador Coast

\Pressure 985 millibars


Antarctic Mapping Mission

The Erebus Ice Tongue in the SAR image above is shown at the bottom of the photo on the right.

Mount Erebus, located on Ross Island, is one of the handful of active volcanoes on the Antarctic Continent. The volcano crater, which routinely spews steam and smoke, is clearly visible in this RADARSAT image. Also visible is the Erebus Ice Tongue, an elongated ribbon of floating ice extruded from the glaciers covering Ross Island. The ice tongue is perforated with crevasses and subsurface ice caverns that can be explored through small openings on the seaward side.

RADARSAT-1

Notes

Slide 12

RADARSAT-1 showed its versatility by imaging the previous unimaged Antarctica continent in September and October of 1997.

This was achieved by yawing the satellite 180 degrees, so that the SAR antenna looked to the left rather than the right. With this manoever, the whole Antarctica continent could be imaged, at the expense of temporarily-reduced coverage of the Canadian Arctic.

Photos courtesy of the Remote Sensing Lab, Byrd Polar Research Center, The Ohio State University.

Page 1 of 1RADARSAT-1 Notes


RADARSAT - 2

Natural Resources Ressources naturellesCanada Canada


RADARSAT-2 Mission Overview

• Data continuity from RADARSAT-1– all RADARSAT-1 SAR imaging modes and

beams supported– plus many additional capabilities

• Launch planned for 2003• Mission duration: 7 years• The next step towards full commercialization

of the RADARSAT programme


RADARSAT-2 Mission Overview

• Mission requirements were developed from market survey

• Features prioritized against revenue potential

• Emphasis on “information content”– maximizing the economic value– expanding the potential for further “Value-Added”

processing


RADARSAT-2 The Future

• Innovations– higher spatial resolution– left or right looking direction– polarimetry

• Plans


RADARSAT-2 Innovations 1

• 3-metre ultra-fine resolution– highest resolution SAR available commercially

• Routine left- or right-looking direction– quicker re-visit time– more responsive to user requests– Antarctic mapping mission fully integrated

• User-selectable polarization (mode-dependant)– selectable polarization (HH or VV or HV or VH or (HH and

HV) or (VV and VH))– quadruple polarization (HH, VV, HV, and VH)– selectable single polarization (HH, VV, HV, or VH)


RADARSAT-2 Innovations 2• GPS receivers on-board

– fast delivery position spacecraft location knowledge is ≤ 20 m (1 sigma).

• ≤ 1 sec delay between imaging in different modes or beams

• Yaw-steering for zero-Doppler shift at beam centre– facilitates processing

• Solid-state data memory• Higher downlink power

– 3-metre minimum size receiving antenna– lower “cost of entry” for new ground stations


RADARSAT-2 Configuration

Solar arrays

SAR Antenna

GPS Antenna

Bus

PSS

Louvers for thermal control

Star Trackers

∆V Thrusters

X-band downlink antenna

CSS -- Sun-seeker Clusters


RADARSAT-2 System Concepts

The Antenna has 10240 Radiating Elements fed by 640 T/R Modules.

By correct phasing of signal to and from each Radiation Element pair, polarizationcan be controlled to achieve H or V on transmit and receive paths

VP PORT

HP PORT

Spacecraft Attitude and Position KnowledgeAttitude Control Accuracy:

Attitude Knowledge Accuracy

Fast Delivery position knowledge

Post Processed PositionKnowledge

±0.05º (3s in each axis)

±0.01º (3s in each axis)

±20m (1s in each axis)

±15m (3s in each axis)

Image AbsoluteLocation Accuracy< 300 m at downlink< 100 m postprocessed

PolePole EquatorYew Steering Supported

• makes processing faster and easier• normalizes doppler bandwidth

Radar OperationPower ON to full Image Capability

Minimum Image Duration:

Maximum Image Duration / Orbit

Gap between imaging in different modes or beams:

10 minutes

5 seconds

28 minutes

1 sec max.

28 minutes

1 PRIGap between imaging in different transmitpolarizations at same PRF

ON Time per orbit

• 2 x 128 GBsolid-state recorders

• on-board GPS

Communications LinksS-Band

UplinkDownlink

32 kbps encrypted128 kbps

105 Mbps encrypted105 Mbps encrypted

X-Band Downlink

Link 1Link 2 Right- and Left-looking directions are both routine

Spacecraft is able to operate in either direction, nominally 10 minutes are requiredfor this manoeuvre

3.9°

- 3.9°


Left- and Right-Looking AntennaRight- and Left-looking directions are both routine

Spacecraft is able to operate in either direction; nominally 10 minutesrequired for the manoeuvre


SAR Antenna

• Antenna has 10240 radiating elements fed by 640 Transmit/Receive modules

• By correct timing of signal to and from each Radiating Element pair,polarization can be controlled to achieve H or V on transmit and receive paths


RADARSAT-2 System Concepts

3-metre resolutionSAR Image

Multi-polarizationSAR Image

Standard Mode Image Quality ParametersThe sum of the azimuth and range ambiguity ratios <-16,5dBGlobal Dynamic Range >30 dBRelative radiometric Accuracy within 100 km by 100 km scene <1 dBOne orbit <1,5 dBThree days <2 dBSpacecraft lifetime <3 dB

Standard Mode Image Quality ParametersThe sum of the azimuth and range ambiguity ratios <-16,5dBGlobal Dynamic Range >30 dBRelative radiometric Accuracy within 100 km by 100 km scene <1 dBOne orbit <1,5 dBThree days <2 dBSpacecraft lifetime <3 dB

All modes available as Left-looking or Right-looking

• Preserves RADARSAT-1 modes with selective polarization• Adds new high resolution and polarimetry• Orbit matches RADARSAT-1• Same Repeat Cycle• Same Ground Track• Same Ascending Node

• Preserves RADARSAT-1 modes with selective polarization• Adds new high resolution and polarimetry• Orbit matches RADARSAT-1• Same Repeat Cycle• Same Ground Track• Same Ascending Node

Sensor ParametersFrequency 5.405 GHzPolarization H, VAccessibility Swath 500 km left and 500 km rightSwath Incidence Angles 20° à 49 °Extended Incidence Angles 10 ° à 20 °, 50 ° à 59 °Noise Equivalent Sigma Zero -21 dB(Standard Mode)

Sensor ParametersFrequency 5.405 GHzPolarization H, VAccessibility Swath 500 km left and 500 km rightSwath Incidence Angles 20° à 49 °Extended Incidence Angles 10 ° à 20 °, 50 ° à 59 °Noise Equivalent Sigma Zero -21 dB(Standard Mode)

Ultra-Fine Narrow

Extended (High incidence)

Extended(Low incidence)

Selective Transmit H or V Receive H or V Polarization or (H and V)

Polarimetry Transmit H and V Receive H and Von alternate pulses on every pulse

Selective Single Transmit H or V Receive H or VPolarization

Subsatellite Track

V

Standard

Wide

ScanSARFine (50km Swath)

Ultra-Fine Wide


RADARSAT-2 Polarization Options

* Approximation

Nominal Swath ApproximateMode Swath coverage to left Resolution:

Width or right of ground range x ground track azimuth

RADARSAT-1 Modes with Standard 100 km 250 km – 750 km 25 m x 28 mSelective Polarization Wide 150 km 250 km – 650 km 25 m x 28 m

Transmit H or V Low Incidence 170 km 125 km – 300 km 40 m x 28 mReceive H or V or (H and V) * High Incidence 70 km 750 km – 1000 km 20 m x 28 m

Fine 50 km 525 km – 750 km 10 m x 9 mScanSAR Wide 500 km 250 km – 750 km 100 m x 100 m

ScanSAR Narrow 300 km 300 km – 720 km 50 m x 50 m

Polarimetry Standard 25 km 250 km – 600 km 25 m x 28 mTransmit H and V on quadruple

alternate pulses polarizationRecieve H and V on Fine, quadruple 25 km 400 km – 600 km 11 m x 9 m

every pulse polarization

Selective Single Polarization Triple Fine 50 km 400 km – 750 km 11 m x 9 mTransmit H or V Ultra-fine Wide 20 km 400 km – 550 km 3 m x 3 mReceive H or V Ultra-fine Narrow 10 km 400 km – 550 km 3 m x 3 m


High-Resolution Comparison

3-metre resolution 10-metre resolution

Source: Sandia National Laboratories


RADARSAT-2 Orbit Characteristics

• Same orbit as RADARSAT-1– 798 km altitude– sun-synchronous “frozen” orbit

• Same repeat cycle and ground track as RADARSAT-1– RADARSAT-1 & 2 scenes precisely aligned


RADARSAT-2 Orbit Characteristics

• 798 km altitude, sun-synchronous dawn-dusk orbit• Same repeat cycle and ground track as RADARSAT-1


0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

-1500 -1000 -500 0 500 1000 1500Initial Distance from Ground Track (km)

Day

s un

til T

arge

t can

be

Imag

ed

RADARSAT-1

RADARSAT-2

RADARSAT-2 Re-Visit Times at EquatorFine Mode


Multipolarized Imagery


ORDER HANDLING

ORDER HANDLING SYSTEM

RADARSAT-2CATALOGUE

SYSTEM

CANADIAN SPACE AGENCY ORDER

DESK

CANADIANGOVERNMENT

USER

COMMERCIAL DISTRIBUTOR

COMMERCIAL USER

SATELLITE CONTROL SYSTEM

SATELLITE CONTROL

PRIME T

T&C STATI

ON

ST. HUBERT

CO-PRIM

E TT&T

STATION S

ASKATOON

EXTERNAL TT&C

STATIONS

MISSION CONTROL FACILITY

CALIBRATION INSTRUMENTS

IMAGE QUALITY CONTROL SYSTEM

OPERATIONS PLANNING

SYSTEM

DATA RECEPTION, ARCHIVING AND PROCESSING

CANADIAN RECEPTION & ARCHIVING SYSTEM

CANADIAN ARCHIVE

FACILITIES

EXTERNAL RECEPTION, ARCHIVING ANDPROCESSING FACILITIES

ARCHIVE FACILITIES

RADARSAT-2PROCESSOR

PRINCE ALBERT

GATINEAU

RADARSAT-2PROCESSOR

VALUE ADDEDRESELLERS

LEGEND

CONTROL AND MONITORINGSAR DATA

RADARSAT-2 Mission Architecture

Source: MDA http://radarsat.mda.ca/


Spacecraft Construction

• Prime contractor: MacDonald Dettwiler & Associates Ltd., Vancouver

• Bus by Aerospazio, Rome, Italy– based on PRIMA modular design

• Payload by EMS (formerly SPAR Aerospace), Montréal

– phased-array antenna– same stowage & deployment as RADARSAT-1

• Launch by Boeing Delta Launch Services Inc.– aboard a Boeing Delta-II rocket from Vandenberg

AFB, California


Assembly, Integration, and TestingRADARSAT-1

Antenna Testing

Thermal-Vacuum Test Chamber

Launch Environment Acoustic Testing


RADARSAT-2 Ground Segment


Ground Segment Functions

• Spacecraft Control

• Operations Planning

• Order Handling

• Reception and Archiving

• Processing and Distribution

• Image Quality Control


Command & Data Handling

• Encrypted command & data communications

• S-band TTC up- and down-link– 32 kbps up-link; 16 or 128 kbps selectable down-link

• X-Band image data down-link– 2 x 105 Mbps links

• Image data stored in Solid State Recorders– 2 x 128 GB


Order Handling

• Graphical preparation of data requests– “Swath Planner” software

• User-selectable– Mode, beam, polarization and look direction– Reception facility– Processing facility

• More responsive to user requests


RADAR Systems


Applications of SAR• Measuring motion of the Earth's surface, to help us better

understand earthquakes and volcanoes and support emergency management efforts.

• Studying the movements and changing size of glaciers and ice floes to help better understand long-term climate variability.

• Developing highly detailed and accurate elevation maps.• Monitoring floods and where they are likely to occur. • Assessing terrain for the likelihood of finding oil or other natural

resources. • Early recognition and monitoring of oil spills.• Assessing the health of crops and forests.• Planning urban development and likely effects.• Studying land cover and land use change.


Geometry of satellite orbit and Earth rotation

Lat and long lines are 10 deg apart

(1100 Km at the equator)

RADARSAT altitude is 800 Km

Inclination is 98 deg, Period is 98 min

The satellite ’moves’ 26 deg west

every orbit (2830 Km at the equator)

Equator

Satellite orbit

Radar beamRadar beam

Imagedswath

Satelliteorbit

Satellite orbit

Radar Beam

Satellite orbit

Radar beamEquator

Imagedswath

Lat and long lines are 10 deg apart (1100 km at the equator)

RADARSAT altitude is 800 kmInclination is 98 deg, Period is 98 min

The satellite ‘ moves ’ 26 deg westevery orbit (2830 km at the equator)


ERS Configuration

SAR Antenna

Solar Panels

ScatterometerAntennas

Bus

Downlink


Previous Satellite SAR Missions• SEASAT 1978• SIR-A 1981• SIR-B 1984• Magellan 1990• ERS-1 1991• J-ERS-1 1992 • SIR-C / X-SAR 1994• RADARSAT-1 1995• ERS-2 1995• Shuttle SRTM 2000


Magellan Mission to Venus 1

Image courtesy of


Magellan Mission to Venus 2

Image courtesy of

Lava domes on surface of Venus imaged by the Magellan radar


Future Satellite SAR Missions

• ENVISAT 2001

• SAOCOM 2002

• ALOS 2002

• RADARSAT-2 2003

• LightSAR


The NASA/DLR SRTM Mission

60-m long boom

Auxiliary radar antennas

Main radar antennas

The Space Shuttle


The ENVISAT Mission 1

ASARImage mode

ASAR GlobalMonitoring Mode MERIS

AATSR

Ground Track


The ENVISAT Mission 2


The ENVISAT Satellite under Construction


The ENVISAT Mission — LEOP Phase

Attitude acquisition

Solar array MEGS release

Wheel controlled fine pointing mode

ASAR instrument deployment

Rate reduction

Solar array

Solar arrayrotation

Kourou

LaunchInjection

L7 stage

Separationsecondary deploymentSolar array

mode

primary deployment

Radar Systems

Notes

Slide 2

These applications have been demonstrated using SEASAT, SIR-B/C, ERS and RADARSAT data. Some applications are still in the research stage, while others, such as ice monitoring, are fully operational today (1999).

The list here came from the LightSAR web pages. They are a list of applications which are expected to be used by the future LightSAR system.

Slide 3

We will often be dealing with satellite SAR data, whose geometry is shown in this slide. The main difference from aircraft SARs is that their coverage pattern is governed by orbit mechanics and by the Earth’s rotation, as illustrated here.

Slide 4

This slide shows the configuration of the ERS-1 (1991) and the nearly identical ERS-2 (1995) satellites. Rather than only a SAR system, they also have a scatterometer and a radar altimeter.

The SAR antenna is 10 m long and 1.2 m wide. The satellite attitude is controlled so that long dimension of the SAR antenna is aligned with the velocity vector of the satellite’s orbit. It can also be steered with a time-varying skew to compensate for the Earth’s rotation. This is called the yaw-steering mode, and it makes the radar beam perpendicular to the satellite ground track, effectively steering the beam to “zero-Doppler”.

The “bus” contains all the electronics and support equipment of the satellite system. This includes items like:

• control computer

• power supply control system

• attitude control system

• radar transmitters and receivers

• radar data handling system

• satellite/earth communications system

Slide 5

The NASA SEASAT mission was the first civilian SAR satellite, and opened up the SAR sensor to the remote sensing community. It only lasted 4 months before an electrical failure shut it down, but in that time an enormous amount of data was collected in North America.

Of particular note to Canada is that a receiving station was built in Newfoundland which operated well throughout the mission, and that engineers at MacDonald Dettwiler were the first in the world

Page 1 of 3Radar Systems Notes

to produce a digital image from a satellite SAR system.

Slide 6

SAR is useful not only on Earth, but has been used by NASA for some of its planetary missions. The most dramatic example is the 1990-92 Magellan Mission to Venus.

Because Venus is perpetually cloud covered, conventional optical instruments could not acquire an image of the surface of Venus.

In the Magellan Mission, an S-band (2 GHz) SAR was used to obtain 100 m resolution images of almost the entire surface of Venus. Scientists used images to understand the geophysical and geological processes on Venus, enhancing our understanding of the solar system.

Slide 7

Many new things were learned from the Magellan data, such as the existence of these lava domes in Alpha Regio region of Venus.

Slide 9

The Shuttle Radar Topography Mission (SRTM) was a joint 11-day shuttle mission (STS-99, Atlantis) of NASA, the U.S. Department of Defense' National Imagery and Mapping Agency (NIMA), DLR, and ASI, the Italian Space Agency. It flew from February 11 to 22, 2000.. Two independent SAR systems, one in C-band (NASA JPL instrument) the other in X-band (DLR/ASI), operated with the main antenna of each instrument located in the open cargo bay of the shuttle, with a second receive antenna mounted on a deployable outboard mast. SRTM represents the first use of fixed baseline single-pass spaceborne InSAR technology with wide-swath scanning SAR and dual frequencies.

The heart of the SRTM is a SAR interferometer using the existing SIR-C/X-SAR hardware in the shuttle cargo bay augmented by secondary C- and X-band receive antennas mounted at the tip of a 60 m boom.

The spatial resolution of the images is 30x30 m, with a circular location error of less than 20 m. The vertical accuracy is < 16 m (90% Linear Error).

Slide 10

Envisat-1 is a multi-sensor satellite mission managed by the European Space Agency. It is scheduled for launch in January 2002.

Envisat-1 will carry an advanced SAR system, called ASAR. It will have various resolutions and swath widths, and will have a ScanSAR mode like RADARSAT. It will have both horizontal and vertical polarization, but not full quad polarization (the HH and VV channels are not mutually coherent).

In addition to the SAR sensor, it will have an advanced along-track scanning radiometer (AATSR), and MERIS, a multi-frequency optical imager.

Slide 11

Have you ever wondered how a satellite with big solar panels and a SAR antenna fits into the launch vehicle ? It’s a tight squeeze !


Envisat-1 will be launched by the French Ariane-5 rocket, which has a cylindrical cargo bay, about 17 m long and 5 m in diameter. Ariane-5 can place two 3000 kg satellites simultaneously or one satellite with a mass of up to 6800 kg in geostationary transfer orbit, compared with a maximum Ariane-4 payload of 4400 kg.

After the rocket has reached its operational altitude, the nose cone is eased off, and the satellite letgo in space. A small rocket on the bottom of the satellite pushes the satellite into its final orbit, usually about 800 Km above the Earth’s surface.

Slide 12

Envisat-1 is now being tested in the Space Laboratory at ESTEC (1999).

ESTEC is the European Space Agency’s main technology centre, and is located in Noordwijk in the Netherlands.

Slide 13

Did you ever wonder how a satellite gets launched ?

This slide shows how the Envisat-1 satellite will be launched from Kourou, French Guyana in January of 2002. LEOP stands for the “Launch and Early Orbit Phase”, and is the most critical period in a satellite’s lifetime.

Note some of the following steps:

• orbit injection (using rockets to get to the final orbit)

• deployment of solar arrays

• locking on to the correct attitude

• deployment of SAR antenna



SAR Image Formation


SAR Image Formation-Outline-

SAR Principles and Geometry

Radar Equation

Antennas, Polarization, Antenna gain, Radar Equation

SAR Real Aperture

Definition, Concept, Geometry, Point Target Backscatter

SAR Processing

Concepts, Range and Azimuth Compression (concepts and diagram), Range and Azimuth Processing

SAR Image Geometry (“High Relief Terrain”)

SAR Properties

Signal level uncertainty, Signal phase uncertainty, Multi-looking


Principle of Synthetic Aperture Radar

Source: CCRS

Flight direction

target


Signal Data

Ground targets in radar remote sensing are illuminated numerous times by the sensor

Before image formation, the collected data are referred to as signal data


SAR Flight Geometry

Source: Adapted from K. Raney

Flight direction

Altit

ude

Slant range

Azimuth

Far range

Near range

Incident Angle

Ground Range

Swath width


AntennasAn antenna couples electromagnetic waves (signals) propagating in free space to and from a transmission line.

frequency dependentdirectionalpolarization dependent

For SAR applications the axis that defines the wave’s electric field orientation with respect to the antenna defines the wave polarization. The general case is elliptical polarized waves.

An antenna focuses the radiated waves into a beam in three dimensions.for efficiency the radiating aperture > 1 wavelengthlarge radiating areas (apertures) can make “tight” beamsthe gain of an antenna is determined by

- electrical losses- beam area (solid angle)


EM Wave PolarizationElectrical Field




Antenna Gain Concepts

(THE ANTENNA “FOCUSES” ELECTROMAGNETIC RADIATION)

12

L 2

L1

POINT RADIATORSOLID ANGLE ILLUMINATED 4

GAIN = 1

FINITE APERTURE RADIATOR

SOLID ANGLE ILLUMINATED 21 2

/ =

1 = K 1 / L1 , 2 = K 2 / L2

GAIN = 4 /

1/2 POWER (-3dB) BOUNDARY

AN ANTENNA GAIN IS AN "ENERGY DISTRIBUTION" GAIN

(THE ANTENNA “FOCUSES” ELECTROMAGNETIC RADIATION)

Φψπ

λΦ ΦΦ Φ π

λ

ψ

Φ

π

K1, K2 - CONSTANTS


Radar EquationA transmitted signal is focused to a beam (solid angle) by an antennaThe signal propagates to the ground at range R as a wave train with spherical phase fronts.The wave electromagnetic fields interact with physical objects in a ground measurement volume to create a distribution of re-radiated waves (scattering).Those secondary waves that propagate towards the receiving antenna provide the received signal.

The ratio of the returned signal power to the power that would have been returned from isotropic scatterers is the radar cross section of the surface.Far from the surface the returning signal fields add to form waves with spherical phase fronts.

The fields of the returning waves that couple into the receiving antenna, and have the correct polarization, define the received signal.The shape of the antenna beam must be taken into account.


Radar EquationILLUMINATION

RECEPTION

SCATTERING


SAR Real ApertureThe Real Aperture of a SAR is the slant range plane interval of the transmitted pulse for which all signals return to the receiving antenna at the same instant of time.

All signals at the same range return to the radar at the same time and are separable only in Doppler shift.

For a transmitted chirp of length τ , the instantaneous radar return at range R contains surface returns corresponding to slant range interval c τ /2, each uniquely coded in chirp frequency.

On a smooth earth, the constant Doppler frequency contours form a family of hyperbolae and the constant range contours form a family of circles.

The real aperture determines the range of influence of a radar saturation event.


The Real Aperture Resolution Cell

All backscatter from this area returns to the radar at the same time

Constant

Range Arc

RealApertureCell


Point Target Echo in a Synthetic Aperture Radar System

AZIMUTH

RANGE POINT TARGET

TRANSMITTEDWAVEFORM

ANTENNA

MOTION DATA RATE = PRF X NUMBER OF RANGE CELLS

POINT TARGETPHASE HISTORY

SPACECRAFT

RANGE

SYNTHETIC APERTURE LENGTH AZIMUTH

DATA RECORDING

CHIRPLENGTH


SAR Processing 1Once the radar illumination beam has passed over a point on the ground, all of the information from that point has been acquired and stored as a two dimensional (range and azimuth) phase history.

In the absence of radar saturation, all of the phase histories of all of the points in the image are linearly combined in a time series to form the SAR “signal” data.

SAR processing decodes the phase signature of each point in range and azimuth and focuses this information into an impulse response. The range and azimuth widths of the impulse response are the range and azimuth resolutions.

Nyquist’s theorem requires that the processed data be sampled at least twice per impulse response width. These samples are the radar image “pixels”.


SAR Processing 2Because the natural coordinates of the range and azimuth data are not separable, the range and azimuth processing steps are coupled.

Range walk and range curvature- resolution vs. beam width- beam squint- Earth rotation

Processing is done in the natural coordinate system of the radar, the slant range plane.

Earth surface presentations of radar images require projection along constant range arcs to the Earth surface elevation at each point. RADARSAT data are often projected to an ellipsoidal Earth model at sea level.


Point Target Compression or Focussing

LOOK 1 LOOK 2 LOOK 3 LOOK 4

AZIMUTHCOMPRESSION RATIO

AZIMUTHCOMPRESSION

AZIMUTHRESOLUTION

CHIRPLENGTH

RANGECOMPRESSION

= SINGLE LOOK APERTURE LENGTHAZIMUTH RESOLUTION

SINGLE LOOKAPERTURE LENGTH

RANGEWALK

RANGECOMPRESSION RATIO

RANGERESOLUTION

CHIRP LENGTH

RANGE RESOLUTION

RANGECURVATURE

=


Pulsed Radars

Radar system transmits a pulse with a long duration

Ground target scatters the transmitted pulse back to the radar

“Range Processing” gathers the many samples of the pulses received and combines them

“Azimuth Processing” gathers the many pulses backscattered by a target and combines them


Range Processing

RANGE - Line of sight between the radar and the illuminated target

RANGE DIRECTION - Perpendicular to flight direction (or azimuth) of the sensor

- Also referred to as the cross-track direction

RANGE RESOLUTION - An image characteristic determined by the system bandwidth or effective length of the pulse


Azimuth Processing

AZIMUTH

AZIMUTH DIRECTION

AZIMUTH RESOLUTION

AZIMUTH COMPRESSION

– Commonly used to indicate the linear distance in the along track direction

– Direction parallel to the line of flight also referred to as the along-track direction

– Resolution characteristic of the azimuth dimension

– Limited by the Doppler bandwidth of the system

– In the SAR signal domain, the raw data are spread out in the range and azimuth directions and must be coherently compressed to realize the full resolution potential of the instrument. Azimuth compression consists of coherently correlating the received signal with the azimuth replica function.


High Relief Terrain Profile with Radar Image Features

MAP PROJECTION

AIR

CR

AFT

ALTI

TUD

E AB

OVE

GR

OU

ND

MOUNTAIN PEAK REFERENCE SURFACE

CONSTANT RANGE ARCS

NADIR VALLEY BOTTOM MOUNTAIN TOP VALLEY BOTTOM

FOREGROUND REFERENCE SURFACE

SLANT RANGE PLANE

FIRST MOUNTAIN RETURN

NADIRLAYOVER

RADAR SHADOW

VALLEY BOTTOM RETURN

AIR

CR

AFT

ALTI

TUD

E AB

OVE

GR

OU

ND


Signal Uncertainty and Signal to Noise Ratios

"TARGET" ELECTRIC FIELD VECTOR

"SIGNAL" TO "NOISE" RATIO IN dB

SIG

NAL

UN

CER

TAIN

TY IN

dB

"NOISE" SPHERE


6

5

4

3

2

1

0 4 6 8 10 12 14 16 18 20


Phase Noise vs Signal to Noise Ratio

"NOISE" SPHERE


SIGNAL TO NOISE RATIO dB

RM

S PH

ASE

NO

ISE

IN D

EGR

EES

5

10

15

20

25

30

35

4 6 8 10 12 14 16 18 20


Multi-Looking Concept

LOOK - Each of the sub-images used to form the output summed image implemented in the processor.

SPECKLE - Statistical fluctuation or uncertainty associated with the brightness of each pixel in a radar image due to coherent illumination and processing


Multi-Looking Concept (cont’d)

Single look image uses all signal returns from a ground target to create a single image

Image will contain speckle but have the highest achievable resolution

Independent images of the same area can be formed in the digital processing of SAR data by using sub-sets of the signal returns

These images are then averaged to create a single multi-look image

Resulting image has lower resolution but reduced speckle


SAR Image Characteristics


SAR Image Characteristics-Outline-

Elements of interpretation

Tone

Texture

SAR image artifacts

Ambiguities

Scalloping

Automatic Gain Control effects


Image Brightness Variations and Interpretation

Two major types of brightness variations observable in a radar image:

variations in tone

variations in texture

Though uncommon, radar artifacts are a potential source of unwanted brightness variation as well

Computers are used to supplement and/or extend our visual interpretation of these brightness variations


Elements of InterpretationInterpretation Example of computerElement interpretation technique

tone → density slicing

colour → multispectral classification

texture → texture analysis

pattern → spatial transforms / classification

size → size feature classification

shape → syntactic classification

association → contextual classification

Source: Manual of Remote Sensing, 1983


Image ToneRefers to each distinguishable grey level from black to white

Proportional to strength of radar backscatter

Relatively smooth targets like calm water appear as dark tones

Diffuse targets like some vegetation appear as intermediate tones

Man-made targets (buildings, ships) may produce bright tones, depending on their shape, orientation and/or constituent materials


Image Tone (cont’d)

Source: CCRS

DARK MEDIUM BRIGHT


Image Texture

Refers to the pattern of spatial tone variations

Function of spatial uniformity of scene targets

For radar images texture consists of scene texture multiplied by speckle

Texture may be described as fine, medium, or coarse


Image Texture (cont’d)

Corn Field Forest

300 m



Source: Ulaby and Dobson, 1989

300 m


SAR Image ArtifactsSAR image artifacts can occur due to platform, sensor, and/or processing problems

Ambiguities - Azimuth Ambiguity- Range Ambiguity- Nadir Ambiguity

ScallopingAutomatic Gain Control effects for RADARSAT-1

Image radiometrics & geometrics can be affected Sometimes reprocessing can improveSometimes incorrigible


Ambiguities

Copy of target appears offset in range and/or in azimuth (ghosting)

Artifacts visible if background is dark and invariant (e.g. calm water), difficult to detect over variable background (e.g. forested land)

Desired signal is contaminated by signal of adjacent targets


AmbiguitiesAzimuth Ambiguity

too slow sampling of returned signals

Range Ambiguitysimultaneous returns from desired illuminated region and of a previously or successively transmitted pulse

- e.g. Nadir Return- return from “under the

satellite” accompanies return from imaged swath

Source: Werle, 1997

Halifax Harbour, Nova ScotiaGhost fleet of ships seen in RADARSAT S7 image

Halifax Harbour, Nova ScotiaGhost fleet of ships seen in RADARSAT S7 image


Nadir Ambiguities

These bright linear features appear at approximately constant range

Signal returns from nadir are strong due to near-specularreflection from targets within a very narrow slant range distance→ bright tone

Due to pulse compression, bright return is restricted to a small number of range cells → sharp, linear shape


Nadir Ambiguities and RADARSAT

Products originally specified as single beam products were designed to avoid nadir ambiguities where possible

Possible location of nadir ambiguities in single beam images:

Near edge of Wide 2 images captured with real-time downlink

Middle of Wide 3 images

Source: Luscombe, 1997


Nadir Ambiguities and RADARSAT (cont’d)

Possible location of nadir ambiguities in multiple beam products:

Within beam Wide 3 in ScanSAR Wide A

In overlap between Wide 1 and 2 beams inScanSAR Wide A and B, and ScanSAR Narrow A

In overlap between Standard 5 and 6 beams inScanSAR Narrow B and ScanSAR Wide B

Source: Luscombe, 1997


ScallopingCaused by improper estimation of DopplerCentroid

Seen as corduroy-likeradiometric banding across the scene (range direction)

Occasionally visible in RADARSAT ScanSAR mode products

Image can be reprocessed using better Doppler Centroidestimates


Automatic Gain Control Effectsof RADARSAT-1

Onboard the sensor, a gain is applied to returned signal data prior to input to the analogue to digital converter (ADC)

Proper selection of gain improves use of limited dynamic range in ADC and minimizes saturation and ADC underflow of signal data

RADARSAT-1 employs an Automatic Gain Control (AGC) whose value is set based on signals received from part of the swath in the half closest to the satellite

If scene is bright in near range and dark in the far range, gain may be too low causing far range targets to appear darker in the image than they should be


Automatic Gain Control Effects(cont’d)

If scene is dark in near range and bright in the far range, gain may be too high causing far range targets to saturate

Saturation often visible as tonal changes appearing in bands across the image in the range direction

Effects of underflow not as visible in image

Both saturation and underflow affect radiometrics of image

For qualitative purposes, AGC banding can be eliminated by ordering image with a constant gain --saturation and underflow may still occur in image


AGC EffectsRADARSAT-1

Beam Mode S5Aug. 21, 1996

Malaysia


Automatic Gain Control Effects(cont’d)

For quantitative analysisPre-Acquisition

- Choose beam and orbit (i.e. ascending, descending pass) that places analysis target in near half of swath

- Banding may still occur in far rangeAcquisition

- use an appropriate fixed gain settingPost-Acquisition

- Perform power loss correction to correct mean value -- requires reprocessing from signal data


Data Products


Data Products-Outline-

Radar Product CharacteristicsSignal Data, Single Look Complex,Georeferenced, Geocoded.

Media ChoicesCD-ROM, Data Cartridge (8mm), CCT,

Hardcopy.

CEOS Standard File Format

Spaceborne SARs

RADARSAT-1 and -2


Introduction

The purpose of this section is to introduce and explain generic radar products and their formats.

For the current radar satellites the generic radar products are very similar in their characteristics, but have different names and acronyms.

The different product names are explained in this section, but all cited examples are for RADARSAT-1 products.


Classes of Radar Product

Signal Data

Georeferenced Products

complex, detected, slant and ground range.

Geocoded Products

detected, ground range.


Signal Data

Raw radar echo data in in-phase and quadrature (I/Q) format

In slant range

Stripped of telemetry format information reassembled into contiguous radar range lines

Not an image, must be processed using a SAR processor to an image product


Georeferenced vs Geocoded ProductsGeoreferenced products:

relative geographic location is incorporated in the image.not corrected to a map projection and should not be used for mapping purposes.

Geocoded products:geometrically corrected to conform to a map projection. often use ground control points and DEM to increase the geocoding accuracy.geocoded products are usually resampled to a standard square pixel size.

See Table 3.1 for an overview of RADARSAT products


Table 3.1 RADARSAT Product Characteristics

Source: RADARSAT International, 1995, RADARSAT Illuminated – Your Guide to Products and Services, RADARSATInternational

Product Name RSI Name Format Mode Pixel SpacingApprox. (m)

# Looks General Characteristics

Single LookComplex(SLC)

Single LookComplex

Slant Range StandardFineWideExtended HighExtended Low

11.6 x 5.14.6 x 5.1

11.6 x 5.111.6 x 5.18.1 x 5.1

1 x 11 x 11 x 11 x 11 x 1

Each pixel is represented by I and Qcomplex data.Must be processed into an image.Retains optimum resolution.

GeoreferencedFine Resolution(SGF)

Path image GroundRange

StandardFineWideExtended HighExtended LowScanSAR NarrowScanSAR Wide

12.5 x 12.56.25 x 6.2512.5 x 12.512.5 x 12.512.5 x 12.5

25 x 2550 x 50

1 x 41 x 11 x 41 x 41 x 42 x 22 x 4

Oriented in orbit path.Must be geometrically corrected ifrequired for mapping.

GeoreferencedExtra-FineResolution(SGX)

Path ImagePlus

GroundRange

StandardFineWideExtended HighExtended Low

8 x 83.125 x 3.125

10 x 108 x 8

10 x 10

1 x 41 x 11 x 41 x 41 x 4

Lower sample spacing than SGFRetains full beam resolution.

SystematicallyGeocoded(SSG)

Map Image GroundRange

StandardFineWideExtended HighExtended Low

12.5 x 12.56.25 x 6.2512.5 x 12.512.5 x 12.512.5 x 12.5

1 x 41 x 11 x 41 x 41 x 4

SGF product is processed to North upand corrected to a map projection.

PrecisionGeocoded(SPG)

PrecisionMapImage

GroundRange

Same as MapImage

Same as MapImage

Same asMapImage

SGF product is corrected using GCPsand a DEM.Best positional product.


Georeferenced ProductsImage Products

Lines and pixels oriented to radar system (e.g. SGF, SGX product for RADARSAT).

- line direction is azimuth direction of radar- pixel direction is range direction of radar

Geographic location of pixels is approximated based on locally spherical elliptical Earth at sea level and typically stored in the product header.

- typically based on orbit models only, no geocoded control points used.

- referred to as systematic georeferenced.Can be in slant or ground range geometry at a variety of pixel spacings.

- variety of terminology used for different satellites (see Table 3.2)


Table 3.2 - Comparable Products Between Different Satellites

RADARSAT ERS -Europe

ERS -North

America

SPOT Landsat

Path Image(SGF)

Precison ImageGeoreferenced(PRI)

GeoreferencedFine Resolution(SGF)

1B

Path OrientedSystematic OR PrecisionCorrection

Path Image Plus(SGX) N/A N/A N/A N/A

Map Image(SSG)

GeocodedImage(GEC)

SystematicallyGeocoded(SSG)

2A Map Oriented SystematicCorrection

Precision MapImage(SPG)

TerrainGeocodedImage(GTC)

PrecisionGeocoded(SPG)

2B Map OrientatedPrecision Correction

Signal Data Raw1 Raw1 1A2 Raw2



Single LookComplex (SLC) N/A N/A

1 SAR Signal Data cannot be viewed as an image2 Optical RAW data (SPOT, Landsat) can be viewed as an image

Source: RADARSAT International, 1995, RADARSAT Illuminated – Your Guide to Products and Services


Geocoded Products

Systematically Geocoded or Map Image (RADARSAT - SSG)

Product is processed to “North Up” and corrected to a map projection.

Image may be converted to one of a large number of map projections.

Sample spacing remains as in original data.


Geocoded ProductsPrecision Geocoded or Precision Map Image (RADARSAT - SPG)

Product is further processed to correct the geographic positional data based on use of Digital Elevation Terrain Model and a number of precisely surveyed ground control points within the imaged area.

Data format and map projections same as for SSG.

Sample spacings remain as in original data.

Variety of terminology used for different satellites (see Table 3.2)


Media ChoicesRadar products are available on a number of media.

Digital (Stored in CEOS format)

- CD-ROM

- Data cartridge (8mm)

- Computer Compatible Tape (CCT) (9 track)

Hardcopy (Available upon request)

- film

- print


Introduction to the CEOS File FormatCEOS, the Committee on Earth Observation Satellites, is an international organisation concerned with various aspects of Earth Observation (EO), including data formats.CEOS has defined an international standard data format that can accommodate all EO data.CEOS is a self defining format and thus there are many minor format variations between CEOS format products. RADARSAT CEOS example:

Consists of 5 files, only one of which contains image data, the other 4 contain information on the image data.Tables 3.3 and 3.4 provide an overview of the structure of the CEOS file format for RADARSAT data.Detailed descriptions of each of the 5 files follows the Tables,using RADARSAT SGX as the example.


Table 3.3 Example CEOS File Format

(SGF, SGX, SLC)

Volume Directory File

SAR Leader File(see Table 3.4)

SAR Data File

SAR Trailer File

Null Volume Directory File

Volume DescriptorFile Pointer RecordText Record

Descriptor RecordProcessed Data

Descriptor Record

Null Volume Descriptor


Table 3.4 Example CEOS File Format

(SGF, SGX, SLC)Descriptor RecordData Set SummaryData Quality SummarySignal Data HistogramProcessed Data (16-bit) HistogramDetailed Processing ParametersPlatform Position DataAttitude DataRadiometric DataRadiometric Compensation Data

SAR Leader File

SAR Data File

SAR Trailer File

Null Volume Directory File

Volume Directory File


Spaceborne SARsThe viewing geometry of a spaceborne SAR, in comparison to an airborne SAR with a similar swath width, varies only a few degrees and thus provides a more uniform illumination geometry over the whole swath.

Depending on the orbital parameters, a spaceborne SAR can collect data more quickly over larger areas than airborne systems.

Frequency of coverage is set by orbit constraints and imaging modes of the radar.

Revisit for typical spaceborne SAR is between 3-35 days.

Corrections must be made in processing for the effects of earth curvature, earth rotation and orbital variations.

The first civilian spaceborne SAR was SEASAT (USA) in 1978, followed by Almaz (USSR/Russia), ERS-1 (Europe), J-ERS-1 (Japan), ERS-2 (Europe) and RADARSAT-1 (Canada).

Tables 3.5 and 3.6 provide an overview of the characteristics of the orbital SAR systems.


Table 3.5 Past Orbital SAR Systems

Parameters Seasat SIR-A SIR-B Almaz SIR-C/X SAR ERS-1 JERS-1

Country USA USA USA USSR USA Europe Japan

Launch Date Jun ‘78 Nov‘81 Oct ‘84 Mar ‘91 Apr ‘94 Jul ’91 Feb ‘92

Lifetime (design) 3months

2.5days 8 days 2 years each 11 days 3 years 2 years

Band L L L S L, C, X C L

Wavelength (cm) 23.5 23.5 23.5 10 23.9, 5.7, 9.6 5.7 23.5

Polarization HH HH HH HH L and C Quad PolX (VV) VV HH

Nominal IncidentAngle (°) 23 50 15 - 64 30 - 60 15 - 50 23 38

Nominal GroundRange Resolution (m) 25 40 25 15 - 30 10 - 26 26 18

Nominal AzimuthResolution (m) 25 40 17 – 58 15 30 28 18

No. of Looks 4 6 4 > 4 4 3 3

Swath Width (km) 100 50 10 - 60 20 - 45 15 – 60 100 75

Repeat Cycle (days) 17, 3 nil nil nil nil 3, 35, 176 44


Table 3.6Current and Planned Orbital SAR Systems

Parameters ERS-2 RADARSAT 1 Envisat 1ASAR

ALOSPALSAR SAOCOM RADARSAT 2

Country Europe Canada Europe Japan Argentina Canada

Launch Date Apr ‘95 Nov. 1995 2001 2002 2003 2003

Lifetime(design) 3 years 5 years 5 years 3-5 years 5 years 5 years

Band C C C L L CWavelength

(cm) 5.7 5.7 5.6 23.6 23 5.6

Polarization VV HH Note 1 Note 2 Note 3 Note 4Nominal

Incident Angle(°)

23 10 – 59 15 – 45 8-60 15-40 10 – 60

NominalGround RangeResolution (m)

26 10 – 100 30 – 1000 10-100 10-100 3 – 100

NominalAzimuth

Resolution (m)28 9 – 100 30 – 1000 10-100 10-100 3 – 100

No. of Looks 3 1 – 8 8 2-8 2-8 1 – 8Swath Width

(km) 100 50 – 500 60 – 405 30-350 35-360 10 – 500

Repeat Cycle(days) 35 24 35 46 7 24

1- Envisat polarizations HH or VV or HH+VV or HH+HV or VV+VH2- ALOS PALSAR polarizations HH or VV or HH+HV or VV+VH or HH+HV+VH+VV 3- SAOCOM polarizations HH or VV or HH+HV or VV+VH or HH+HV+VH+VV4- RADARSAT-2 polarizations HH or VV or HV or VH or HH+HV or VV+VH or HH+HV+VH+VV


RADARSAT 1Canada’s first Earth observation satellite, RADARSAT 1, was launched in November of 1995.

The radar is C-band (5.3 GHz, 5.66 cm wavelength) with HH polarization.

The system has six imaging modes with a diverse range of incident angles and swath widths as illustrated in Figure 3.1.

Technical details of the SAR imaging modes are shown in Table 3.7.

More flexibility in image resolution, incident angles and swath width are possible with this system compared to other operational SAR systems

– Nominal ground resolution ranges from 8 - 100 metres– Incident angles range from 10 – 59 degrees– Swath width ranges from 50 – 500 km


Standard

WideScanSAR

Satellite groundtrack

Extended - Low incidence

Fine

Extended - High incidence

Figure 3.1RADARSAT 1 SAR Imaging Modes


Table 3.7RADARSAT-1 SAR Imaging Modes

APPROXIMATE NOMINAL APPROXIMATE NUMBER OFMODE BEAM & INCIDENT ANGLES GROUND a AREA PROCESSED

POSITION (DEGREES) RESOLUTION (M) (KM) LOOKSFine F1 near 36.4 - 39.6 8 50 X 50 1 X 1(15 positions) F1 36.8 - 39.9 SGF or SGX

F1 far 37.2 - 40.3F2 near 38.8 - 41.8F2 near 39.2 - 42.1F2 far 39.6 - 42.5

F3 near 41.1 - 43.7F3 41.5 - 44.0

F3 far 41.8 - 44.3F4 near 43.1 - 45.5

F4 43.5 - 45.8F4 far 43.8 - 46.1

F5 near 45.0 - 47.2F5 45.3 - 47.5

F5 far 45.6 - 47.8Standard Mode S1 20 - 27 25 100 x 100 1 x 4(7 beams) S2 24 - 31 SGF or SGX

S3 30 - 37S4 34 - 40S5 36 - 42S6 41 - 46S7 45 - 49


SGF = SAR Georeferenced Fine Resolution Product = Path ImageSGX = SAR Georeferenced Extra Fine Resolution Product = Path Image Plus


Table 3.7 (cont’d)RADARSAT-1 SAR Imaging Modes

APPROXIMATE NOMINAL APPROXIMATE NUMBER OFMODE BEAM & INCIDENT ANGLE GROUNDa AREA PROCESSED

POSITION (DEGREES) RESOLUTION (M) (KM) LOOKSWide W1 20 - 31 30 165 x 165 1 x 4(3 positions) W2 31 - 39 150 x 150 SGF or SGX

W3 39 - 45 130 x 130ScanSAR Narrow SCNA 20 - 40 50 300 x 300 2 x 2

SCNB 31 - 46 SCNScanSAR Wide SCWA 20 - 49 100 500 x 500 2 x 4

SCWB 20 - 46 450 x 450 SCWExtended High EH1 49 - 52 25 75 x 75 1 x 4(6 beams) EH2 50 - 53 SGF or SGX

EH3 52 - 55EH4 54 - 57EH5 56 - 58EH6 57 - 59

Extended Low EL1 10 - 23 30 170 x 170 1 x 4SGF or SGX

SGF = SAR Georeferenced Fine Resolution Product (Path Image)SGX = SAR Georeferenced Extra Fine Resolution Product (Path Image Plus)SCN = ScanSAR Narrow Beam Product (Path Image)SCW = ScanSAR Wide Beam Product (Path Image)



RADARSAT 2

MDA selected to build, own and operate RADARSAT 2.

Launch is scheduled for 2003.

C-band system including beam modes of RADARSAT 1 as outlined in Figure 3.1 with significant extensions.

RADARSAT 2 has several major improvements over RADARSAT 1:

Polarizations - horizontal (HH), vertical (VV) and cross (HV, VH) polarizations including polarimetry.

3 metre resolution with new ultra-fine beam mode

Increased revisit using dual-sided (left and right) imaging.


Image Quality and Calibration


Image Quality and Calibration-Outline-

β o, γ o, and σ o

How do they differ?How to get these from DN (Digital Number) on product?

Digital Numbers on the Products and β o

Look Up Tables- Why?- Types

RADARSAT Image CalibrationProcessor Functionality

- Antenna Pattern Correction


β o, σ o, γ o and RADARSAT Data

Backscatter

β o per unit area in slant range

σ o per unit area in ground range

γ o per unit area of the incident wavefront (perpendicular to slant range)


β ο, σ ο, γ ο and RADARSAT Data

Source: R.K. Raney, 1998 N.B. Geometry Approximations


β o, σ o , γ ο and RADARSAT Data

where j = range sample

k = azimuth sampleθj = incident angle

σ ojk = RADAR BACKSCATTER COEFFICIENT ([dB])

β ojk = RADAR BRIGHTNESS ([dB])

Most natural radiometric observable of a RADAR- “backscatter per unit area in slant range”

Requires no knowledge of local incident angle γ ο

jk = GAMMA ([dB])

( )( )0 01010*log sinjk jk jσ β θ= +

( )( )0 01010*log tanjk jk jγ β θ= +and


β ο, σ ο , γ ο and RADARSAT Data(Cont’d)

Incident angle (θj )

should be local incident angle

often use model geoid at sea level to define θj

- approximate

- may be significant radiometric approximation

- may lead to significant error in backscatter coefficient


β ο and RADARSAT Detected Products

where:

DNjk

- Digital Number at range j, azimuth k

A3, A2j

- Constant + Range Dependent LUT

- supplied with CEOS Product (subsampled)

- interpolated between values and extrapolated at end

( )20

10

310*log

2jk

jk

j

DN AA

β +

=


β ο and RADARSAT Detected Products (cont’d)

A3, A2j

Radiometric Data Record: - lookup_tab, samp_inc, offset

References of product specifications:RADARSAT International, RADARSAT Data Products Specificiations RSI-GS-026 Version3/0, May 8, 2000http://www.rsi.ca/adro/adro/tools/tools/cdpf_specs/d4_3-0.doc

Updated information for Section 5 and Appendix D can be found in:ALTRIX Systems, "Extraction of Beta-Nought and Sigma-Nought from RADARSAT CDPF Products," CSA Document AS97-5001, Rev. 4, April 28, 2000.http://www.space.gc.ca/csa_sectors/earth_environment/radarsat/radarsat_info/description/radio_calib.asp


β ο and RADARSAT SLC Products

where:

DNIjk / DNQjk

- Real / Imaginary Part Digital Number at range j, azimuth k

A2j

- Range Dependent LUT supplied with CEOS Product and interpolated to each range sample

( )2 2010

/ 2/ 2

10*log

jk jk j

jk jk j

jk jk jk

I DNI AQ DNQ A

I Qβ

=

=

= +


Look Up Tables (LUTs)

Applied during conversion of calibrated floating point data at last stage in processing to DN(in Canadian Data Processing Facility (CDPF))

for storage and transfer (exabyte and CD-ROM)

Included in “the picture” if DN 2 (or DN ) used directly

Reverse to β o for quantitative analysis


LUTs (cont’d)

Aim to create LUTs to ensure best use of 8 or 16 bits on storage media

Range dependent (because β ο is range dependent)

All values below lower limit β ο (β οl) and above upper limit β ο (β οu ) will be given limiting values (β ο l or β οu respectively)

knowledge of these β ο will be lost


LUTs (cont’d)

Defined for specific applications

Sea, Ice, Land, Mixed, Others (Point Target, Unity)

may show saturation/underflow if different target in same image

- easily checked by looking at DN

Reference: User Guide at Order Desk


Sample CDPF LUT for 8-bit products vs. Incident Angle

Source: Canadian Space Agency

LUT

(dB)

Incident Angle (deg)


Conversions from DN to β � and σ o

Performed by Third-Party Software

Requires approximations

to algorithms in processor

to geometry of imaging

Reference: Shepherd, N., ALTRIX Systems, "Extraction of Beta-Nought and Sigma-Nought from RADARSAT CDPF Products," CSA Doc ument AS97-5001, Rev. 4, April 28, 2000.Produced under contract to S. Srivastava, Canadian Space Agency


Image Quality Results

How good is RADARSAT ?

Results Obtained by CSA during the Beam Qualification Phase (Prior to April 1, 1996)

see next viewgraph


Measured RADARSAT Image Quality

Examples of measured and specified parameters for sample images of the RADARSAT Precision Transponder sites. Descriptive parameters include Pass Type (A for ascending pass and D for descending pass) and Orbit Parameters Used (D for definitive orbit data and P for predicted orbit data). The measured image quality parameters presented include Impulse Response Width (IRW) in Range and Azimuth, Peak Side Lobe Ratio (PSLR) in Range and Azimuth, and Absolute Location Error (ALE). The measured values are better then the specifications.

Source: S.K. Srivastava, T.I. Lukowski, R.B. Gray, N.W. Shepherd, B. Banik, R.K Hawkins and C. Cloutier, “Calibration and Image Quality Performance Results of RADARSAT,” Advances in Space Research, Vol. 19, No. 9, pp. 1447-1454, 1997.

Beam Type F2 S2 S7Chirp Bandwidth (MHz) 30 17.28 11.58

Acquisitition Date March 5, 1996 March 9, 1996 March 5, 1996

Orbit (Pass Type) 1749 (A) 1799 (D) 1742(D)

Orbit Parameters Used D P D

Incident Angle (deg) 40.35 26.63 47.39

Product Type SGX SGX SGX

Parameter Meas. Specification Meas. Specification Meas. SpecificationRange IRW (m) 8.07 9.74 21.13 24.31 19.07 22.06

Azimuth IRW (m) 7.76 9.0 25.65 28.0 24.83 28.0

Range PSLR (dB) -19.23 -18.0 -21.69 -18.0 -22.22 -18.0

Azimuth PSLR (dB) -21.69 -18.0 -21.58 -18.0 -22.49 -18.0

Abs. Location Error (m) 27.7 750 52.9 750 45.3 750


Geometric Location Accuracy

Based on Processor Accuracy in determination of geometry of lines at zero-Doppler (output product)

Assumes all targets and imagery at zero height ASL

will be significantly different in ‘‘real cases’’


Radiometric Calibration

Dependent on imaging system and processor

Account for imaging and processing parameters of system and microwave propagation

Calculations performed during the processing


Radiometric Calibration (cont’d)

where:

PT transmitter power

g(θj ) one way antenna gain pattern

GSYS system gains

R slant range

( ) ( )0 32

1 1 1T SYSj

RP Gg

β αθ


RADARSAT Relative Radiometric Accuracy

Design Goals for Standard Beam Modes

100 km * 100 km scene 1.0 dB

one orbit 1.5 dB

three days 2.0 dB

mission lifetime 3.0 dB


Antenna Pattern


Determination of Antenna Gain Pattern in Elevation for Standard Beam S1

Elevation (deg)

22

20

18

16

14

1216 17 18 19 20 21 22 23 24

22

20

18

16

14

1216 17 18 19 20 21 22 23 24

Elevation (deg)

Abs

Gai

n Fa

ctor

(db)

Abs

Gai

n Fa

ctor

(db)

D=-0.3344 +/-0.04864dB

Reference:T. I. Lukowski, R.K. Hawkins, C. Cloutier, J. Wolfe, L.D. Teany, S.K. Srivastava, B. Banik, R. Jha and M.Adamovic, “RADARSAT Antenna Pattern Determination, ” Proceedings of GER’97, Ottawa, May 27-29, 1997.


Antenna Pattern in ElevationCross-track illumination variation

Critical to Radiometric Calibration

correction for gain variation within beam and between beams

major contributor to radiometric budget

Determine separately, then apply to each image in the processor

“all done” before user gets the data; nothing for user to do (for RADARSAT)


Antenna Gain Pattern “Correction”

Application of antenna gain pattern to product to reverse illumination variation at imaging

Requires accurate knowledge of geometry and satellite attitude to find angles at each range in image

assumes that all imaging is for flat terrain at sea level


Use of Radiometrically Calibrated Products

Goal of radiometric calibration to account for all the contributions in the radiometric values not due to the target characteristics, so that the backscatter values of targets can be compared to one another or a reference

Radar data and calculations are not “perfect”

uncertainities in the radiometric values may be increased by further processing by the userwhen relating radiometric values to ground measurements, uncertainties in both must be considered


Radiometric Enhancement


Radiometric Enhancement-Outline-

FilteringSpeckle Reduction

- Definition; Why speckle filtering; What is the ideal speckle reduction filter

- Non-adaptive filters (FFT filters)- Adaptive filters (Frost, Lee, MAP Gamma filters)

Edge Detection- Ratio edge detector filter- Touzi filter


Radiometric Enhancement (cont’d)-Outline-

Analysis of Image Texture

Visual Enhancement

Contrast Enhancement

Linear Enhancement

Nonlinear Enhancement

- Histogram, Exponential, Logarithmic, Power Law Stretch


IntroductionThis section reviews the methods of enhancing the radiometrics of an image using speckle reduction filters, spatial enhancement filters and visual enhancements.

The understanding of radar “speckle” is key to the understanding of SAR and SAR radiometric enhancements.

Often the reduction of speckle is desired to improve classification and/or for enhancement.

To reduce speckle, adaptive filters (e.g. map gamma filter), should be used rather than non-adaptive filters (e.g. FFT filters) on radar imagery.

Adaptive filters take into account the local properties of the terrain backscatter or the nature of the sensor, whereas non-adaptive filters do not.


Introduction to Speckle

Image variance or “speckle” is a granular noise that inherently exists in SAR imagery (Figure 5.1).

Speckle gives a single look image a grainy, salt and pepper appearance and is the dominating factor in radar imagery.

Speckle noise occupies a wider dynamic range than the scene content itself.

Images processed with a small number of 'looks' will have distribution intensities which are quite asymmetric due to speckle noise.

Creating a symmetrical histogram may not be the optimum procedure. Instead, pixels are set to the extreme limits of thedata intensity distribution (e.g. DN values of 0 and 255 for 8-bit data).

For a detailed review of speckle, see Raney (1998).


What is Speckle?

Speckle is coherent interference of waves scattered from terrainelements observed in each resolution cell.

An incident radar wave interacts with each element of the surface and surface cover to generate scattered waves propagating in all directions.

Those scattered waves that reach the receiving antenna are summed in direction and phase to make the received signal. The relative phase components contain the differential propagation paths.

The SAR focusing operation coherently combines the received signals to form the image.

The scattered wave phase addition results in both constructive and destructive interference of individual scattered returns and randomly modulates the strength of the signal in each resolution cell.


Figure 5.1 - Example of Speckle


What is Speckle? (cont’d)

Addition of backscatter from a collection of scatterers produces random constructive and destructive interference, see Figure 5.2.

Constructive interference is an increase from the mean intensity and produces bright pixels.

Destructive interference is a decrease from the mean intensity and produces dark pixels.

These random fluctuations give rise to speckle.

Reducing these effects enhances radiometric resolution at the expense of spatial resolution.


Figure 5.2 - SpeckleConstructive Interference

Destructive Interference

Result

Result

Example of Homogenous Target

Constructive interference

Destructive interference

Varying degrees of interference(between constructive and destructive )

Coherentradar waves


Speckle Suppression

Speckle results from a coherent (phase included) process.

Speckle can be reduced by incoherent (amplitude or power) processes.

Speckle reduction (or smoothing) necessarily reduces the resolution (increases the resolution cell size) of single channel SAR data.

Two basic linear processes:

- Multi-look - divides the signal into minimally overlapped frequency bands, processes each to a reduced resolution image, registers these, detects and adds the detected images. Examples of multi-look processing are shown in Figure 5.3.

- Averaging - detects the full resolution image, performs local averaging and resampling processes to create reduced resolution,reduced speckle images.

- For distributed targets both processes are equivalent.


Figure 5.3 - Multi-look ProcessingExamples of multi-look processing. Note that image chips A, B, and C all have the same resolution, but that image chips C and D have comparable image quality factors (data from an X-band airborne SAR, 1972, optically processed).(In Principles & Applications of Imaging Radar, Manual of Remote Sensing, 1998, Chapter 2 - Raney, pg. 75)Courtesy R.

Shuchman and E. Kasischke,

ERIM

A 6.1 m x 6.1 m N = 1

QSAR = 0.027

C 6.1 m x 6.1 m N = 16

QSAR = 0.43

B 6.1 m x 6.1 m N = 4

QSAR = 0.11

D 1.5 m x 2.13 m N = 1

QSAR = 0.31


Why Speckle Filtering?

The presence of speckle noise must be considered when selecting analysis methodologies.

Speckle filtering will permit:

better discrimination of scene targets.

easier automatic image segmentation.

the application of the classical enhancement tools developed for imagery from optical sensors such as; edge detectors, per-pixel and textural classifiers.


The Ideal Speckle Reduction Filter

Reduce speckle with minimum loss of information

In homogeneous areas, the filter should preserve:

radiometric information

edges between different areas

In textured areas, the filter should preserve:

radiometric information

spatial signal variability: textural information


Families of Speckle Reduction FiltersNon-adaptive filters

The parameters of the whole image signal are considered.Do not take into consideration the local properties of the terrain backscatter or the nature of the sensor. Not appropriate for filtering of non-stationary scene signal. Examples are the FFT filters.

Adaptive filtersAccommodate changes in local properties of the terrain backscatter.

- The speckle noise is modelled as being stationary - The target signal is not stationary since the mean backscatter

changes with the type of targetExamples are the Frost, Lee, Map Gamma, local mean and local median filters

Figure 5.4 shows examples of adaptive filters.


Figure 5.4 - Gamma vs. Median Filter

Tapajós, BrazilMay 20, 1996 Beam F2

Original Image

Median 5x5

Map Gamma 5x5


Kernel Size

Examples of Mean, Median and Mode filter kernels (or windows) are shown in Figure 5.5.

Filters are a sub-array of X by Y pixels that moves through the image.

All three filters shown in Figure 5.5 are square box filters, with a kernel size of 3 by 3 pixels

Degree of smoothing is a function of the size of the kernel.

As filter kernel size increases, smoothing increases.


Figure 5.5 - Filtering Kernel

Source: CCRS

5 7 49 8 65 5 8

MEAN

5 7 49 8 65 5 8

MEDIAN

5 7 49 8 65 5 8

MODE

5+7+4+9+8+6+5+5+8= 5757÷ 9 = MEAN = 6

4,5,5,5,6,7,8,8,9MEDIAN = 6

45556 MODE = 57889

3 x 35 x 5

7 x 7


Mean and Median FiltersPrinciple

Intensity at each sample interval in the image is replaced by the mean of pixel values in a moving window surrounding the sample.

The box or mean filter preserves well the radiometry but blurs textured areas.The median filter assigns the window median value to each sample.

Preserves texture information better Modifies the radiometric information of homogeneous areas, and does not preserve point target signature

Not recommended for radar imagery.See Figure 5.6 for examples of both filters.


Figure 5.6 - Median and Mean Filters


Original Image

Median 7x7

Mean 7x7


Adaptive Filtering

Adaptive filters (e.g. Map Gamma) reduce speckle while preserving the edges (sharp contrast variation).

Adaptive filters modify the image based on statistics extracted from the local environment of each pixel.

Larger kernel size (e.g. 11x11) result in an important increased smoothing effect on the resulting image (Figure 5.7).


Figure 5.7 - Gamma Filter


Original Image

Map Gamma 7x7

Map Gamma 11x11


Advantages of Adaptive Filters

Most of the well known adaptative filters require the calculation of the local observed mean and normalized standard deviation (coefficient of variation).

The adaptive filter produces an accurate estimate of the backscattering coefficient inside homogeneous (stationary) areas while preserving edge and texture structure in nonstationary scenes.


Most Well-known Filters: The Frost Filter

Principle

The unspeckled pixel value is estimated using a subwindow of the processing window.

The size of the subwindow varies as a function of target local heterogeneity measured with coefficient of variation:

– the larger the coefficient of variation, the narrower the processing subwindow

The Enhanced Frost Filter (Lopes, Touzi and Nezri, IEEE, 1990) minimizes the loss of radiometric and textural information (Figure 5.8).


Figure 5.8 - Examples of Filters


Most Well-known Filters : The Lee Filter

Principle

The unspeckled pixel value is a weighted sum of the observed (central) pixel value and the mean value.

The weighting coefficient is a function of local target heterogeneity measured with the coefficient of variation.

The Enhanced Lee Filter (Lopes, Touzi and Nezri, IEEE, 1990) minimizes the loss of radiometric and textural information (Figure 5.8).

The Enhanced Lee and Enhanced Frost Filters perform similarly.


Most Well-known Filters : The MAP Gamma Filter

Background

The Frost and Lee filters are based on models which do not use the statistical properties of the underlying scene.

In a joint study with CESR (Toulouse, France), CCRS participated in the development of the MAP Gamma Filter (Lopes, Touzi, Nezri and Low, IJRS, 1993).


Most well known Filters : The MAP Gamma Filter (cont’d)

Principle

The filter is based on the assumption that the (unspeckled) intensity of the underlying scene is gamma distributed.

The filter minimizes the loss of texture information better than the Frost and Lee filters within gamma distributed scenes.

It is suitable for a wide range of gamma distributed scenes, such as forested areas, agriculture areas, and oceans.

The filter preserves the observed pixel value for non-gamma distributed scenes.

See Figure 5.9 for the filter example.


Figure 5.9 - Map Gamma Filter


Original Image Map Gamma 11x11


Effects of Filtering

Whereas, adaptive filters (Lee, Frost and Gamma) preserve the mean value and are therefore preferable for SAR imagery (Figure 5.10).

Figure 5.11 shows that as the filter kernel size increases, so does the percent change in standard deviation.

A quantitative example of these effects on real data is shown in Figure 5.12.


Figure 5.10 - Effects of Filtering

Filter Size & Type vs % Change in Mean

Median 5x5

Filter Size & Type

Perc

enta

ge C

hang

e in

Mea

n

Median 7x7Median 3x3

Lee 5x5

Raw Lee 7x7Lee 3x3 Frost 3x3

Frost 7x7

Frost 5x5

Source: CCRS


Figure 5.11 - Effects of FilteringFilter Size & Type vs % Change in SD

Filter Size & Type

% C

hang

e in

Sta

ndar

d D

evia

tion

Raw Median 7x7 Lee 7x7 Frost 7x7

Frost 3x3

Lee 5x5

Lee 3x3Median 3x3

Median 5x5 Frost 5x5

Source: CCRS


Figure 5.12 - Effects of Filtering

Source: CCRS, Brown et al, 1993

Effects of Filtering on Sample Wheat Field Statistics, ERS-1 SAR

Mean Standard Deviation

% Change in Mean

% Change in SD Mean/SD

Raw

Median 3x3

Median 5.5

Median 7x7

Lee 3x3

Lee 5x5

Lee 7x7

Frost 3x3

Frost 5x5

Frost 7x7


Edge Detection in SAR ImagesApplication : Segmentation of the image into separate entities, classification Types of Edge Detection Filters:

Directional, Gradient, Laplacian, Sobel, Prewitt, Ratio Edge Detector

WarningsThe classical edge detectors (e.g. Gradient, Sobel) developed for imagery from optical sensors are not suitable for SAR images.Because of the multiplicative nature of speckle, they detect more false edges within brighter areas.Imagery must first be filtered (Gamma) prior to using the classical edge detectors.


Edge Detection in SAR Images (cont’d)

Potential alternatives

The ratio edge detector (R. Touzi et al., IEEE TGRS, 1988) is suitable for SAR images and does not require pre-filtering.

Performance of the ratio edge detector is better since information is lost during pre-filtering for the classical edge detectors.


Ratio Edge Detector Filter

(Touzi, et. al., 1998)

Original SAR image

Gradient image (5x5)

Ratio Edge Detector (5x5)

- For the gradient detector, the probability that a pixel of a homogeneousarea is assigned to edges (Pfa) is dependent on the mean power due to themultiplicative nature of the noise.

- The operator detects more false edges in brighter areas.

- The ratio edge detector is the ratio of the average of pixel values of twononoverlapping neighborhoods on opposite sides of the point.

- The Pfa does not depend on the mean power

- The performance of the ratio edge detector is a function of the size ofneighborhoods, the number of looks and the ratio of the mean powers.


The Touzi multi-resolution speckle FilterAll the most well known adaptive filters were developed under the assumption that the signal is stationary within the moving processing window of a fixed size (i.e. its mean and variance do not vary within the observation time).

The filters are not effective primarily when applied to fine structures such as roads and trails which are generally smoothed out by thefilters.

A new multi-resolution filter the Touzi Filter (Figures 5.13 and 5.14) was developed at CCRS (a part of PCI software 2002 version).

The size and the shape of the filter processing window are adapted to signal nonstationarity. The Touzi multi-resolution ratio edge detector is used for better filtering of contours and edges (Touzi et al., IEEE TGRS 1998)This permits more efficient speckle reduction and a better preservation of the scene spatial variations (texture, edges, point targets).

Source: R. Touzi, CEOS workshop 1999


Figure 5.13 - Touzi Filter


Original Image Touzi filter


Figure 5.14 - Touzi Filter

Original ImageTouzi filter

15X15

Lee filter7X7

RADARSAT-1 imageFine Mode


Introduction to Texture

Texture is the spatial variation of tones in an image.

Image texture may be qualitatively described as having properties like fineness, coarseness, smoothness, granulation, randomness, lineation, mottled, irregular, hummocky (Figure 5.15).

In a SAR image, texture has two components: (1) spatial variability in the scattering properties of the scene and (2) speckle.


Figure 5.15 - Image Texture

Corn Field Forest

300 m



300 m

Source: Ulaby and Dobson, 1989


Texture Analysis

TextureTextural features contain information about the spatial distribution of tonal variations.Methods available:

Co-occurrence matrix (GLCM)Grey level difference vector (GLDV)Lacunarity (gap analysis)Neighbouring grey level dependence matrix (NGLDM)Spatial correlation functionModel-based approaches


Texture Analysis (cont’d)

Texture

Textural features statistics can be extracted using agrey level Co-Occurrence Matrix (GLCM).

User specific neighborhood parameters.

Examples of features from GLCM:

- Homogeneity - Mean - Contrast - Standard deviation - Dissimilarity - Entropy - Angular second moment - Correlation

Speckle suppression techniques may not preserve all scene texture details.


Contrast Stretch

A contrast stretch enhances visual interpretation (Figure 5.16).Matches data’s dynamic range to dynamic range of display.Involves the construction of a look-up table (LUT).LUT is a graphical model of the mathematical function selected.


Figure 5.16 - Contrast Stretch

Original image Linear Stretch

Rosario, Argentina


Linear Stretch

Effective upper and lower cutoff values are established.

Upper and lower histogram values are set to maximum & minimum limits respectively.

May use full or piecewise stretch.

Balance of the data are stretched linearly to fill the expanded display range.

See Figure 5.17.


Figure 5.17 - Linear Stretch


Nonlinear Enhancements

Distort the image radiometry.

Useful only for visual interpretation.

quantitative radiometric information can be lost.

spatial information is preserved.

results may not be replicable from scene to scene.


Histogram Stretch

Input display range may not be fully utilized.

Output display range makes full use of the dynamic range.

Enhances the contrast where frequency of occurrence is greatest.

Options include:- Inverse frequency- Frequency equalization- Gaussian normalization- Histogram matching


Inverse Frequency (or Infrequency)

Produce an image in which the bright pixels represent those grey levels in the original image which were infrequent.

LUT is derived from an inverted (upside down) histogram of the input image data values.

Useful for highlighting rare or small features in an image (lineaments or edges).

Figure 5.18 is an example of infrequency enhancement.


Figure 5.81 - Inverse Frequency Enhancement


Frequency Equalization

Redistribute pixel values so that there are approximately the same number of pixels for each data value available.

More for visual display than for image analysis.

Figure 5.19 is an example of Frequency Equalization.


Figure 5.19 - Frequency Equalization


Exponential Stretch

High-range brightness is enhanced and high histogram skew can be corrected.

Details in the higher part of the dynamic range are revealed.

An example of an algorithm for this stretch is ex.


Logarithmic Stretch

Low-range brightness is enhanced and histogram skew may be corrected.

Skewness is common and may invalidate some image analysis algorithms which assume a normal data distribution.

Also known as root Enhancement.

Root ( log N).

Tends to lend an overall brightening to the resultant image (see figure 5.20).


Figure 5.20 - Logarithmic Stretch


Power Law Stretch

Changes the image brightness, S, as a power law:

Snew = Sn

n > 1 enhances strong returns at the expense of weak returns.

n < 1 ( n ) enhances weak returns at the expense of strong returns.

The special case n = 2 converts a magnitude image to a power image.

Alters the probability distribution (histogram) of the data and may invalidate processes based on Gaussian assumptions.


CONVERSION FROM DN TO:

σ° or β°(dB)

σ° or β°(power)

INTERFEROMETRY- DEM generation- Coherence image- Surface change detection

FILTER(speckle reduction)- Adaptive filters

- Non adaptive filters

STEREOSCOPY- DEM generation- Planimetric feature

extraction

CHANGE DETECTION(e.g. ratio, difference)

CALCULATION OFTARGET SIGNATURES

CONVERT POWERVALUES TO dB

e.g. σ° (dB) = 10 log10 ( X )

MODELLING- Theoretical backscatter- Geophysical parameters

extraction

TEXTURE ANALYSIS(input for classification) FILTER

(speckle reduction)- Adaptive filters- Non adaptive filters

ENHANCEMENT(for visual interpretation)- High pass filters- Low pass filters- FFT filters- Contrast stretch

GEOMETRIC CORRECTION- Ortho-rectification using DEM- Slant / ground range conversion- Polynomial transformation

DATA FUSION- RGB-IHS Colour Space- Principal Component

Analysis- Vector Overlay

CLASSIFICATION- Supervised- Unsupervised

ACCURACY ASSESSMENT

AUTOMATED FEATUREEXTRACTION- image thresholding- edge detection, lineaments- directional filters (Sobel, etc.,)

OTHER DATA- multitemporal SAR- optical RS- geophysical- Thematic polygons

or vectors (GIS)- etc.

QUANTITATIVEQUALITATIVE

“TYPICAL” SAR IMAGE PROCESSING METHODOLOGY

INFORMATIONEXTRACTION

- Valued-addedinformation map

AMPLITUDEDigital Number

(DN)

AMPLITUDE + PHASESingle Look Complex

(DNI + DNQ)

STEREOSCOPY- terrain interpretation


Geometric Characteristics


Geometric Characteristics- Outline -

Review of Platform / Target GeometryImage Acquisition

Relief displacement (foreshortening, layover, shadowing)

Radiometric Distortion (local incident angle, image brightness)

Geometric CorrectionPrinciple of SAR Geocoding

Methods Available (Slant to Ground Range, Polynomial Method, Radargrammetric Method)

Digital Elevation Model Error Propagation on the Ortho-Image

Error Sources and Propagation

Image Resampling


Geometric Characteristics- Outline -

Radar Stereoscopy

Dichotomy, Consequences, Configurations, Compromise, Guidelines

Interferometry

Geometry, Critical Issues


Introduction

The intent of this section is to describe the geometric characteristics of SAR, including viewing geometry, target interaction, geometric correction, stereoscopy and interferometry.

Geometric characteristics are very different to optical remote sensing and are key to understanding radar remote sensing.

Radar systems are side-looking distance measuring systems, thus key geometric parameters are the incident angle, local incident angle and look direction.

The side-looking geometry of radar results in several geometric distortions, such as slant range scale distortions and relief distortions.


SAR GeometryWhat is it ?

Review of Platform - Target GeometryImplications of SAR Geometry

- displacement (foreshortening, layover, shadowing, Earth curvature)

- radiometry

How to correct it ?Geometric Correction MethodsImage Resampling Algorithms

How to exploit it ?StereogrammetryInterferometry


Geometry of Synthetic Aperture Radar (SAR)

Figure 6.1 shows the geometric characteristics of a SAR. Incident angle (θ°) is the angle between the radar line-of-site and the local vertical with respect to the geoid. Incident angle is the most important parameter describing the relative geometry between the radar and the observed scene.System altitude alters incident angle and thus viewing geometry.Azimuth direction is the flight direction, or along-track direction.Range direction is the across-track direction.Slant range is the distance measured along a line between the antenna and the target.Ground range is the distance from the ground track to an object.Near range is the part of the radar image closest to the flight path or nadir, whereas far range is the part of the radar image farthest from the flight path.


Figure 6.1 Geometry of SAR

Flight direction

Altit

ude

Slant range

Azimuth

Far range

Near range

Incident Angle

Ground Range

Swath width


Comparison of Imaging Geometries

System altitude has a large effect on the imaging geometry of the SAR.

Spaceborne systems operate between 600-800 km, whereas airborne systems between 3-12 km.

Figure 6.2 shows airborne systems would cover a larger range of incident angles (15°-60°) than spaceborne systems (37°-40°).

Higher altitude of spaceborne systems means incident angles are usually steeper.


Figure 6.2 Comparison of Imaging Geometries

airborne 10 – 100 kmspaceborne 25 – >500 km

IMAGE SWATH

SPACEBORNE SAR

AIRBORNE SAR


Geometric Distortions

Slant range acquisition

Relief displacement

layover

foreshortening

shadowing

NOTE: All these geometric distortions have effects on radiometry


Radar Slant Range / Ground Range

Radar can be presented in either slant or ground range, as shown in Figures 6.3 to 6.5.

Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object.

Ground range is slant range projected onto the geoid of the Earth.

Slant range data can be converted to ground range by resampling.


Figure 6.3 High Relief Terrain Profile with Radar Image Features

GROUND RANGE PLANE

AIR

CR

AFT

ALTI

TUD

E AB

OVE

GR

OU

ND

NADIR VALLEY BOTTOM VALLEY BOTTOMMOUNTAIN TOP

FOREGROUNDREFERENCE SURFACE

MOUNTAIN PEAKREFERENCE SURFACE

CONSTANTRANGE ARCS

SLANT RANGE PLANE

FIRST MOUNTAIN RETURN

VALLEY BOTTOM RETURNRADAR SHADOW

NADIRLAYOVER


Figure 6.4 Radar Slant Range / Ground Range

Slant Range (rR )

Ground Range (rGR) rGR


Figure 6.5 Slant Range vs Ground Range Radar Imagery


Resolution Cell

Resolution is the minimum distance that describes how well the radar can discriminate closely spaced reflectors.

Resolution cell is 3-dimensional in the illuminated space.

The area of the rectangle in Figure 6.6 is called the resolution cell.

rA is the azimuth resolution and rR is the range resolution.

Source: Raney, 1998


Figure 6.6 Resolution Cell

rR = range resolution rA = azimuth resolution

Source: Raney, 1998


Look Direction

Look direction is defined as the angle in the horizontal plane in which the radar antenna is pointing when transmitting a pulse and receiving the return signal from the ground or from an object.

Unless perfectly symmetrical or perfectly random, targets have a preferred orientation.

For example, opposing look directions for agricultural fields may produce different tones on the image due to row direction related to planting, tilling, or harvesting.

In areas with high relief, opposing look directions are often necessary to fill in areas of radar shadow.

On fixed looking systems, such as RADARSAT, two look directions can be acquired using ascending (east-looking) and descending (west-looking) passes, see Figure 6.7.


Figure 6.7 Look DirectionSarawak (Malaysia)


Slant / Ground Range Resolution

Figure 6.8 shows the scale differences in slant range (rR) and ground range (rGR) images.

Differences between slant and ground range resolution are highest at small incident angles.

For example, Figure 6.8 shows that rR at 10° is 10 m, while the rGR at 10° is 29 m. At 70°, rR and rGRconverge.


Figure 6.8 Slant and Ground Range Resolution

-


Local Incident Angle

Figure 6.9 shows that the local incident angle (θloc) is defined as the angle between the radar line-of-sight to the line normal (or orthogonal) to the local slope.

θi is the flat-earth or ellipsoid incident angle.

Local incident angle can have a large effect on image brightness per pixel.

Local incident angle is the largest source of error in radiometric calibration.


Figure 6.9 Local Incident Angle

Source: Raney, 1998Source: Raney, 1998


Effect of Topography / Local Incident Angle on Image Brightness

Local topographic slope (Figure 6.10) can have a significant effect on image brightness.

Local topographic slope causes changes in local incident angles.

A small local incident angle results in brighter radar returns.

A larger local incident angle results in darker radar returns.

Slope-induced radiometric effects are useful for some applications such as geomorphology and geology.


Figure 6.10 Image Brightness as an Effect of Topography

Radar Shadow

θlocBrighter -smaller localincident angle

Nominal Brightness

Darker -larger local

incident angle

θloc

θloc


Incident Angle for Microwave Scattering:Actual vs Processor

Most satellite processors assume sea-level, ellipsoid earth models for geometric and radiometric calculations (see Figure 6.11).

Thus, almost all images over land have inaccuracies due to terrain effects (see Figures 6.12 and 6.13).


Figure 6.11 Incident Angle for Microwave Scattering:Actual vs Processor

Actual Terrain

Model Geoidat Sea Level

actual

assumed


Figure 6.12 Local Incident Angle Effects

LOCAL INCIDENT ANGLE EFFECTS


Topographic Displacement

Due to the different imaging geometries of radar and optical systems, as seen in Figures 6.13 and 6.14, topographic displacement differs between the systems.

Horizontal displacement for a radar sensor is highest near nadir, and decreases with incident angle (Figure 6.13).

Horizontal displacement can be severe at small incident angles (see Figure 6.13).

In contrast, topographic displacement for optical systems (Figure 6.14) increases with incident angle.


Figure 6.13 Topographic Displacement - Radar Sensor

Source: Toutin, Th. and Y. Carbonneau, 1992, “MOS and SEASAT Image Geometric Correction”, IEEE-TGARS, Vol. 30, No. 3, pp. 603-609.

θ

θ


mountain top

reference surface orthographicprojection ofmountain top

Horizontal displacement of a 100m mountain top (m)airborne

satellite

radar ground rangeprojection of mountain top


Figure 6.14 Topographic Displacement - Optical Sensor

Optical Sensor

by similar triangles

reference surface

Optical SensorHorizontal displacement of a 100m mountain top (m)

nadir

θ

θθ


Geometric Distortion - Shadow

Radar shadow indicates areas on the ground not illuminated by the radar because of viewing geometry and scene relief (Figure 6.15).

Since no return signal is received, radar shadow appears very dark in tone in the imagery (Figure 6.16).

Radar shadow is most common in steep terrain imaged at large incident angles.

The height of an object (building, bridge, etc.) can be obtained from its radar shadow.


Figure 6.15 Radar Shadow

Source: Raney, 1998

illumination

wave

front

distortion shadow

scene


Figure 6.16 Radar Shadow in Airborne SAR Image of Folded Sandstone Beds


Geometric Distortion - Foreshortening

Foreshortening is the appearance of compression in topographic features in the scene.

The horizontal displacement resulting from the small incident angles causes foreshortening of the slope facing the radar.

The features appear to be tilted toward the radar (Figures 6.17 and 6.18).

Foreshortening is at a maximum when a steep slope is orthogonal to the radar beam.


Figure 6.17 Foreshortening

scene

displacement

illumination

wavefr

ont

foreshorteningSource: Raney, 1998


Figure 6.18 Foreshortening

Source : DeSève, Toutin & Desjardins, IJRS, 17(1):131-142, 1996.


Geometric Distortion - LayoverLayover is an extreme case of foreshortening, and occurs when the incident angle is smaller than the local topographic slope (Figure 6.19).

Extreme horizontal displacement causes the top of the mountain to be mapped “overlaying” the fore slope (Figure 6.20).

In the layover case there is no radar shadow, but severe elevation displacement and layover of the foreslope.

Difficult for interpretation since each pixel may contain scatter from more than one area.


Figure 6.19 Layover

ilumination

distortion

wavefront

scene

layover

θi

Source: Raney, 1998


Figure 6.20 Layover Effects on SAR Imagery (Lima, Peru)


Geometric Correction

Geometric correction includes slant to ground range, registration, and local incident angle corrections (if topographic information is available).

Allows a correspondence between the position of points on the final image and their location in a given cartographic projection.

Consists of introducing spatial shifts on the original image (Figure 6.21).


Geometric Correction (cont’d)

Algorithms are classified into three methods

Slant to ground method (zero relief)

Polynomial method (best fit approximations)

Radargrammetric method (known sensor geometry)

The last method uses terrain elevation information.

Elevation information (DEM) is required to correct the distortions caused by topographic displacements.

All methods use a resampling kernel during the rectification of the images.


Geometric Correction (cont’d)

Radiometric distortions also exist in connection with terrain relief and cannot be completely corrected.

Resampling of the image can introduce radiometric errors.

A layover/shadowing mask and a local incident angles map are both helpful for many applications.

Ground Control Points (GCPs) are used to establish and/or refine the transformation.


Figure 6.21 Principle of SAR Image GeocodingGrey value Interpolation (Resampling)

Radar Image

Grey Value Assignment Map to ImageTransformations

Digital ElevationModel

GeocodedImage


Slant / Ground Range Conversion

SAR data are acquired in slant range.

Slant to ground range conversion is used to project the acquired image to the ground system.

Need to know (or assume) imaging geometry, platform altitude, range delay and terrain elevation.

Resampling used to give uniform pixel spacing (in ground range) across the image swath.


Slant / Ground Range Conversion

Slant to ground range conversion can be done during signal processing or during image processing.

Generally applied after radiometric correction.

Approach and algorithms used are a function of analysis objectives.

RADARSAT ground range products assume a sea level ellipsoid earth model with zero relief.


Image Registration Polynomial Transforms

Polynomial transform uses a best-fit.

Figure 6.22 shows how the uncorrected image changes to fit a map projection using various orders.

Note the 1st order is a shift-rotation of the image, whereas the 3rd order is a complex warping of the image.

1st order polynomial transforms are adequate for images which only require a shift-rotation and a change of scale.

2nd order polynomials are used for images requiring non-linear warping.

3rd and higher order polynomials create a more complex image transformation.


Figure 6.22 Image Registration Polynomial Transforms

1st order 2nd order 3rd order

Corrected Image

Uncorrected ImageSource: PCI, Chapter 6, 1997


Order of Polynomial Transformation

Higher order transforms require a greater number of ground control points (GCPs) in order to produce the transform model.

High order does not guarantee higher accuracy.

Higher order usually ties the image down at the GCPs, but can increase errors between the GCPs.


Radargrammetric Method

Analytical formulation of the distortions during image formation.

relative to the platform (ephemeris and ancillary data)

relative to the sensor (integration time, pulse length, depression angle)

relative to the Earth (geoid, relief)

Output is an “Ortho-image” corrected for all distortions, including relief.


Radargrammetric Method - Advantages

Unified reference: cartographic system.Image to terrain correction.Only one resampling for an image(slant range to map projection directly, no intermediate conversion to ground required).Homogeneity in the ortho - image generation.Use of a DEM or a mean altitude.Better integration with GIS or digital maps.Comprehension and control of the full geometric process and of the resulting errors.Figure 6.23 is a comparison of two geocoding techniques.


Figure 6.23 Comparison of Two Geocoding Methods


Radargrammetric Method

In addition to GCPs, this method requires platform and sensor information and DEM or mean elevation data.

Orbit and sensor information is usually available in radar product headers (e.g. RADARSAT CEOS Leader File).

The planimetric accuracy of the final ortho-image is dependent on the accuracy of GCPs and the DEM.

Figure 6.24 gives the curves representing the planimetric error of the RADARSAT ortho-image as a function of viewing angle (incidence) and DEM accuracy.


Figure 6.24 Planimetric Error of Ortho-images

RADARSATBeam Modes

Viewing Angle (degrees)

DEM

Acc

urac

y (m

etre

s)

Plan

imet

ricEr

ror (

met

res)

Fine

Standard

WideSource: Toutin, 1995


Planimetric Error of Ortho-ImagesStudy Cases

Case #1

Situation

- Desire a 20 metre planimetric accuracy and the user has a DEM with 10 metre accuracy (elevation)

Potential options for RADARSAT acquisition

- any mode beyond 25°

Case #2

Situation

- The user has a DEM with 40 metre accuracy (elevation) and has acquired a RADARSAT Fine 3

Predicted best planimetric accuracy

- 35 metres

Source: Toutin and Rivard, Canadian Journal of Remote Sensing, 23(1) 63-70, 1997


Figure 6.25 Planimetric Error of Ortho-images

Source: Source: Toutin, 1995Toutin, 1995Source: Toutin, 1995

RADARSATBeam Modes

Viewing Angle (degrees)

DEM

Acc

urac

y (m

etre

s)

Plan

imet

ricEr

ror (

met

res)

Fine

Standard

Wide

Case #1

Case #2


Sources and Propagation of Errors

Approximations in mathematical modelling

Position and definition of the GCPs on the image

Cartographic coordinates of the GCPs (planimetric and altimetric)

Inaccuracies or errors in the DEM

Resampling kernel


Comparison of the Current Approaches

Polynomial Method Does not model the viewing geometry• Not related to the distortions• Does not introduce ephemeris data• Does not use DEM• Corrects image locally at the GCPs• May require many GCPs• Sensitive to GCP distribution

Radargrammetric MethodModels the viewing geometry

• Reflects the distortions• Uses ephemeris data

• Uses DEM• Corrects the image globally

• Needs few (5-8) GCPs• Not sensitive to GCP distribution


Geocoding Summary

Geocoding is the geometric correction of image data to a map projection.

Traditional method of geocoding is the polynomial transform. This method does not model the viewing geometry or use elevation data to correct for topography.

The most accurate geocoding method is the radargrammetric method.

The main advantages of the radargrammetric method are that it models the viewing geometry, uses satellite ephemeris data and elevation data to correct for topography.


Image Resampling Algorithms

Pixels in the input image are not in the same orientation (and sometimes spacing) as the output image so pixels must be “resampled”.

Resampling involves the extraction and interpolation of digital numbers (DN) from the uncorrected image to their calculated location in the corrected image.

Figure 6.26 shows how the cells in the corrected matrix do not match the corresponding cells of the uncorrected matrix.


Image Resampling Algorithms

New digital numbers (DNs) must be assigned by an interpolation of the pixel values surrounding the calculated position.

Filtering should occur during resampling to avoid multiple resampling of the imagery, which can degrade and reduce interpretability of the imagery.

Main interpolation algorithms are:Nearest NeighbourBilinear InterpolationCubic ConvolutionSinx / x

Nearest neighbour interpolation is not recommended for radar since it can lead to artifacts and distorted image statistics.


Figure 6.26 Image Resampling Bilinear Interpolation

Geometrically Correct Matrix

UncorrectedMatrix

Source: PCI, Chapter 6, 1997


Bilinear Interpolation

Calculates grey level as a weighted average of the four nearest pixels in the uncorrected image, where the closest of the four has the highest weighting and the farthest having the lowest.

Not optimal for noisy (speckle) radar images.

Can smooth the appearance of output image.

Alters grey values.

Blurs edges in image and decreases resolution.


Cubic Convolution

Uses a weighted average of sixteen surrounding pixels to approximate the digital value of the corrected output image.

Good output registration and appearance.


Sinx / x

Uses a weighted sampling in the shape of sinx / x function to calculate output image.

Typically 18 or 16 pixels wide.

Provides optimal radiometric and geometric accuracies.

Up to 30 times higher computational requirement compared to Nearest Neighbour.


Resampling - Summary

Nearest neighbour resampling should not be used for radar imagery.

Sinx / x or Cubic Convolution is recommended for radar imagery.

Multi-resampling degrades image radiometry and reduces interpretability.

Filtering should be performed during the geometric correction step to avoid multiple resampling.


Radar Stereoscopy

Stereo viewing reproduces the natural process of stereo vision.

Natural stereo process needs two images acquired from “slightly” different locations (different incident angles).

More natural with VIR than SAR images.

Enables extraction of planimetric features in a cartographic coordinate system without a DEM.


Radar Stereoscopy (cont’d)To perceive and extract qualitative and/or quantitative information in the user reference system.

qualitative

- analysis and interpretation

quantitative

- planimetry (road, lake, power line..)

- altimetry (relative or absolute)

Planimetric information accuracy is independent of altimetric accuracy.


Radar Stereoscopy Consequences

Reported stereo results are variable.

Practical experiments do not clearly support theoretical expectations, especially in rough topography.

Theoretical modeling accounts for geometric error propagation and not radiometric image content.

Radiometric differences between images have more impact on SAR than on optical imagery.

A compromise has to be reached between the geometric and radiometric properties of the stereo pair.


Stereo Configurations

Viewing the same scene from different look directions or incident angles can cause the images to appear very different. This can make stereo viewing and point matching more difficult than with optical imagery.

Radiometric disparities are tonal differences between the scenes in a stereo pair resulting from differences in viewing geometry (e.g., shadow, brightness change due to local incident angle).

Geometric disparities are geometric differences between the scenes in a stereo pair resulting from differences in viewing geometry. They are a necessary part of the stereo process because they introduce image parallax. However, severe geometric disparities can make features unrecognizable between scenes.


Stereo Configurations (cont’d)

In SAR images, greater geometric disparities normally introduce greater radiometric disparities, and thus make image matching more difficult.

A compromise between the geometric and radiometric disparities is required for successful stereo from SAR.

The compromise is very dependent on the topography relief of the area being viewed.


Stereo Configurations (cont’d)

Figure 6.27 shows radar parallax with opposite- and same-side SAR configurations.

The radar parallax for the opposite-side example is large, but the geometric and radiometric disparities are also large.

The radar parallax for the same-side example is small, but the geometric and radiometric disparities are also small.

Thus there are trade-offs between geometric and radiometric issues (Figure 6.28).


Figure 6.27 Stereo Configurations

Opposite Side Same Side

Large geometric disparitiesLarge radiometric disparities

Small geometric disparitiesSmall radiometric disparities

SOLUTION


Figure 6.28 Radar Stereoscopy Compromise

COMPROMISE

Point matching

Parallax Stereo Intersection Map

coordinates

Manualor

Automated

Radiometric Errors

Least-squareAdjustments

Geometricserrors

RadiometricDisparities

GeometricDisparitiesTERRAIN

COMPROMISE

APPLICATIONS

Less quantitybut

Better quality

More quantitybut

Poorer quality


Radar StereoscopyGeneral Guidelines for DEM Extraction

Source : Toutin, IEEE-TGARS, 37(5):2227-2238, 1999

TerrainSlopes

Flat 0°-10°

Rolling10°-30°

Mountainous30°-50°

Radiometricdisparities Small Medium LargeGeometricdisparities SmallMediumLarge

CompromisesSame side, large intersection angle

Opposite sides,small look angles

Same side, small intersection angle and

large look angles

StereoRADARSAT

Configurations

S1 asc - S1desc S7 - S1 (asc or desc) S7 - S4 (asc or desc)

F1 asc - F1 desc F5 - F1 (asc or desc) F4 - F1 (asc or desc)


Repeat Pass Interferometry

Based on two image acquisitions of the same scene from slightly displaced orbits of the satellite

Phase information of the two image data files are then superimposed

The two phase values at each pixel are subtracted, leading to an interferogram that records only the differences in phase between the two original images

Phase differences can be related to the altitude variation at each position in the swath and enable the production of a Digital Elevation Model (DEM)


RADARSAT Interferometry Limitations

Critical issues or requirementsmust use single beam, SLC products

no change in backscatter, preferably dry, to maintain the coherence (vegetated sites a problem)

results can be affected by anisotropic propagation of one or both of the data takes (mainly variation in atmospheric water vapour content)

for topographic mapping RADARSAT orbits should be approximately 0.5 - 1.5 km apart

for detection of feature movement orbits should be as close as possible

ground control points required

knowledge of sensor location critical; orbit selection important


RADARSAT Interferometry Limitations (cont’d)

With good baseline and coherence, the technique could be better than stereo (~ 10 m vertical accuracy)


CONVERSION FROM DN TO:

σ° or β°(dB)

σ° or β°(power)

INTERFEROMETRY- DEM generation- Coherence image- Surface change detection

FILTER(speckle reduction)- Adaptive filters- Non adaptive filters

STEREOSCOPY- DEM generation- Planimetric feature

extraction

CHANGE DETECTION(e.g. ratio, difference)

CALCULATION OFTARGET SIGNATURES

CONVERT POWERVALUES TO dB

e.g. σ° (dB) = 10 log10 ( X )

MODELING- Theoretical backscatter- Geophysical parameters

extraction

TEXTURE ANALYSIS(input for classification) FILTER

(speckle reduction)- Adaptive filters- Non adaptive filters

ENHANCEMENT(for visual interpretation)- High pass filters- Low pass filters- fft filters- Contrast stretch

GEOMETRIC CORRECTION- Ortho-rectification using DEM- Slant / ground range conversion- Polynomial transformation

DATA FUSION- RGB-IHS Colour Space- Principal Component

Analysis- Vector Overlay

CLASSIFICATION- Supervised- Unsupervised

ACCURACY ASSESSMENT

AUTOMATED FEATUREEXTRACTION- image thresholding- edge detection, lineaments- directional filters (Sobel, etc.,)

OTHER DATA- multi-temporal SAR- optical RS- geophysical- Thematic polygons

or vectors (GIS)- etc.

QUANTITATIVEQUALITATIVE

“TYPICAL” SAR IMAGE PROCESSING METHODOLOGY

INFORMATIONEXTRACTION

- Valued-addedinformation map

AMPLITUDEDigital Number

(DN)

AMPLITUDE + PHASESingle Look Complex

(DNI + DNQ)

STEREOSCOPY- terrain interpretation


Classification and Information Extraction


Classification TechniquesSupervised and Unsupervised ClassificationClassification AlgorithmsAccuracy AssessmentMaximum Likelihood Classification ExampleNew Classification Approaches

Change DetectionDifference ImageRatio ImageClassification ComparisonChange Vector Analysis

Classification and Information Extraction (Image Exploitation)

- Outline -


Data Integration

RGB Colour Space

IHS Colour Space

Principal Component Analysis

Classification and Information Extraction (Image Exploitation)

- Outline -


Introduction

Currently the majority of operational classification and information extraction is performed using manual interpretation approaches.

Manual approaches tend to be very time consuming and expensive.

Several successful automated approaches are operational, such as flood mapping.

Emerging techniques will increase the use of automated approaches in the future.

This section reports on the automated quantitative approaches using calibrated data.


Introduction - Classification

Image classification categorizes image pixels into classes producing a thematic representation.

Classification performed on single or multiple image channels to separate areas according to their different scattering or spectral characteristics.

Classified data can be used in thematic maps, imported into a GIS or can be further incorporated into digital analysis.

Thematic maps provide an interpretable summary of classes enabling analysts to associate detection capabilities of SAR imagery with terrain features.

Digital image classification procedures are differentiated as being either supervised or unsupervised (clustering).


Supervised Classification

Requires image analyst to “train” the computer to recognize a set of pixels with similar signatures.

Encompasses three components:- training area selection

- classification

- post-classification analysis and accuracy assessment

Analyst determines the best classification scheme to meet objectives and applies knowledge of the site during the training process.

Figure 7.1 illustrates examples of supervised classification.


Figure 7.1 - Examples of Classifications


Training Area Selection

Training areas are small samples of homogeneous areas selected by the image analyst prior to classification.

Appropriate training areas are determined from maps, ground data, interpreted stereo airphoto or other information.

Training areas should be:Free of anomalies

Large enough to provide good statistical class representation

Sufficient in number to account for small local variations


Training Area Selection(cont'd)

Training areas should avoid:

Edge pixels containing the combined backscatter of multiple targets

Inconsistencies within the area such as roadways, powerlines, intermittent cover, etc.

Once defined, training areas are used to generate signature statistics for each defined class.

Class signatures include class means and a class covariance matrix.


Supervised Classification

Figure 7.2 illustrates the problem with supervised classification using linear boundaries for classes.

The separation of the major classes with a minimum of error is possible with an n-dimensional decision boundary.

The graph uses 1 band and 2 classes to illustrate how the overlapping areas of both class 1 and class 2 distributions have erroneously classified pixels.


Figure 7.2 - Supervised Classification

Source: Jensen, 1996

Pixels in class 2 erroneously assigned to class 1

Pixels in class 1 erroneouslyAssigned to class 2

One dimensional decision boundary

Num

ber o

f pix

els

Class 1 Class 2


Classification Strategies and Algorithms

During classification, each pixel is compared to each of the class signatures.

Comparison performed by computer using a predetermined classification algorithm.

Most commonly used classifiers in remote sensing are:

Minimum Distance (to Means) Classifier Parallelepiped Classifier

Maximum Likelihood Classifier (MLC)

Once a pixel has been assigned to a class, it is given the class value in the corresponding cell of the "classified" image.


Minimum Distance (to Means) Classifier

Simplest algorithm and thus low computational time.

Determines each pixel's "distance" from class means, and assigns them to the closest class, see Figure 7.3.

If pixel is further than the analyst defined distance from any category, it remains unclassified or "unknown”.

Classifier does not evaluate differing degrees of within class variance, therefore has lower overall accuracy than MLC.


Figure 7.3 - Classification Algorithms


Channel A Channel A

Cha

nnel

B

Cha

nnel

B

Minimum distanceclassifier

ParallelepipedClassifier

Channel A

Cha

nnel

B

Maximum LikelihoodClassifier


Parallelepiped Classifier

Used when multi-band imagery is available.

Parallelepiped classifier is more sensitive to within class variance.

Algorithm considers range of values within each category of the training set, denoted as minimum and maximum value for each image band (appears as a rectangle in Figure 7.3).

Range limits define small decision region with clear class segmentation compared to minimum distance classifier.


Parallelepiped Classifier(cont’d)

Outliers can increase the decision region inappropriately causing errors of commission.

Problems occur when classes overlap, as in Figure 7.3.

These pixels are labelled as overlap and are caused from class distributions exhibiting correlations poorly described by the rectangular decision regions.

Low computational requirement with adequate classification accuracies.


Maximum Likelihood Classifiers

Assumes that the training statistics for each class have a normal or "Gaussian" distribution

NOTE: Radar statistics are often non-”Gaussian”

Uses training statistics to compute a probability value of whether it belongs to a particular land cover category class

Training statistics with bi- or tri- modal histograms are not suitable as they indicate non-homogeneity within classes and are non-”Gaussian”


Maximum Likelihood Classifiers(cont'd)

Examines the probability function of a pixel for each of the classes, and assigns the pixel to the class with the highest probability.

Usually provides the highest classification accuracies.

Larger number of computations required to classify each pixel, resulting in a high computational requirement.

Can use a-priori knowledge to weight probability function.


Unsupervised Classification (Clustering)

Unsupervised classification does not require training areas or analyst's knowledge of area

Creates natural groupings present in the image values

Values with similar grey levels are assumed to belong to the same cover type

Analyst must determine the identity of the computer derived spectral clusters

Principal clustering algorithms includeK- means clusteringISODATA clusteringNarendra-Goldberg clustering

See figure 7.1 for an example of unsupervised classification


Post-Classification Filtering

Resulting classification image map may be difficult to interpret.

Classified data have a salt-and-pepper appearance due to inherent variability of the per-pixel classifier.

Post-classification filtering removes pixels and pixel groups not satisfying a minimum requirement.

Figure 7.4 is an example of a mode post-classification filter, where the pixel is reassigned to the surrounding class majority pixels.

Post-classification filtering usually enhances interpretability and increases classification accuracy.


Figure 7.4 Post-Classification Mode Filtering


Pixel (7,4) and

3x3 windowClassified Image

ModeFilteredResult


Classification Accuracy Assessment

Evaluate accuracy of the classification procedure by checking against known homogeneous areas.

Overall accuracy vs. accuracy by class.

Results of the accuracy can be displayed in a confusion matrix, such as Figure 7.5.

Confusion matrix plots known pixels against classified pixels.

Errors of Commission

Pixels incorrectly assigned to a particular class that actually belong in other classes, see the lower left half of confusion matrix in Figure 7.5.

Errors of Omission

Pixels incorrectly excluded from a particular class, see the upper right half of confusion matrix in Figure 7.5.


Grains Corn BareSoil

Soybeans Water Forest Urban

Grains 60.6 13.0 26.2 0.1 0.0 0.1 0.0

Corn 1.8 70.7 25.9 0.2 0.0 1.4 0.0

Bare Soil 2.3 17.1 80.1 0.3 0.0 0.2 0.0

Soy beans 0.0 0.0 0.3 96.0 0.0 0.2 3.4

Water 0.0 0.0 0.0 0.0 98.3 0.0 1.7

Forest 17.8 15.9 14.7 2.6 0.0 47.8 1.2

Urban 0.1 0.0 1.1 5.0 0.0 1.8 91.9

Average Accuracy: 77.92% Kappa Coefficient: 0.61889Overall Accuracy: 70.09% Standard Deviation: 0.00391

Figure 7.5 Maximum Likelihood Confusion Matrix

Classification of 3 bands: C-HH, C-HV, C-VV


New Classification ApproachesIntroduction

Supervised and unsupervised classifications generally use "per-pixel" approach.

Due to radar speckle, SAR classification is often done on a per field or polygon basis using either the thresholding technique or polygon averages.

Newer classification methods associate pixels with their surrounding neighbours similar to classification performed by human visual interpretation.


New Classification Approaches

NEURAL NETWORKSBuilding block design algorithm imitating a "human" decision-making process to classification.

Do not make assumptions about the underlying distribution of the data

Uses both spectral and textural patterns in the classification process.

Major advantage is that it can identify subtle and non-linear patterns that traditional classifiers do not detect.

Problem of neural networks is that it can be very difficult to train.


New Classification Approaches

FUZZY LOGICSimulates vagueness or uncertainty encountered in nature

Categorizes data according to non-discrete class structure

CONTEXTUAL CLASSIFIERSClassification of a pixel is influenced by the class(es) assigned to its neighbours Pixel is examined in "context" to surrounding pixels

Numerous other specialized classifiers available.


Change Detection

Change detection methodologies useful forUrbanizationAgricultural DevelopmentForest Land ManagementIce Forecasting, etc.

Utilizes two or more scenes covering same geographic area acquired over a temporal period

Channels of data from one pass or one instrument

Two different passes, same radar, same scene, same mode

Two different passes, different radars, same scene


Change Detection(cont'd)

Spaceborne SAR is ideal due to high revisit capability.

Must consider SAR properties:Imaging geometryRelief displacementImage to image registrationCalibration requirements.

Must also consider:Environmental conditions (precipitation, moisture conditions of vegetation and soil, snow cover, etc.) Time of year.


Change Detection(cont'd)

Change Detection Methodologies:Difference Image

Ratio Image

Classification Comparison

Change Vector Analysis

Figure 7.6 is an example of change detection using different colors for each radar image


Figure 7.6 - Change Detection


Difference Image

This task should be done in the power domain. This task should only be carried out on "sets of pixels" or a "per field basis”.Difference image is created by subtracting the mean value of parcels of pixels in two different images of the same area.Results in either a positive or negative value where change has occurred.Zero values indicate parcels of no change.Must consider threshold boundaries between change and no-change.Must consider calibration issues.


Ratio Image

Ratio image is created by dividing the mean value of parcels of pixels in two different images of the same area.

For SAR imagery, ratios can only be constructed from multi-look images and should be in the power domain.

Band ratios deliver the combined information content of two image bands.

Ratios can help minimize unwanted information and/or noise.

System and processing effects must be considered when producing ratios.

Ratio images must be scaled to produce an acceptable product for visual interpretation.

Interpreting ratio images requires a knowledge of target reflectance illumination and ground conditions.


Classification (Thematic) Comparison

Compare the thematic output from two or more sources.

Identifies areas of change and the nature of change(e.g. from agriculture to urban).

Accuracy depends upon initial classification accuracies of input imagery; any errors are compounded.


Change Vector Analysis

Uses spectral or spatial differences to detect a change or disturbance.

SAR requires data sets acquired at separate times.

Plotted against each other on a graph, the two spectral variables will show the magnitude and direction of change from the 1st to the 2nd date, see “A” in Figure 7.7.

The vector describing direction and magnitude of change from the 1st to the 2nd date is the spectral change vector.

Decision that a “change” has occurred is made if a threshold is exceeded, as in “C” and “D” in Figure 7.7.

Source: Jensen, 1996


Figure 7.7: Change Vector Analysis

Source: Jensen, 1996Source: Jensen, 1996

B LITTLE OR

NOT CHANGE

YEAR 1YEAR 2

THRESHOLD

C CHANGE

(e.g. CLEARED FOR SUBDIVISION)

YEAR 1YEAR 2

THRESHOLD

YEAR 1

YEAR 2

DECISIONTHRESHOLD

A SPECTRAL CHANGE

VECTOR

SPEC

TRAL

VAR

IABL

E Y

SPECTRAL VARIABLE X

D CHANGE

(e.g. REGROWTH OF NATURAL VEGETATION)

MAGNITUDE OF CHANGE

ANGLE OF CHANGE


Data Integration

Remotely sensed data can contribute in a variety of ways to resource management activities involving multiple data sets.

Multiple data sets provide Spatial continuity and geometric flexibility

Multi-temporal coverage

Complete coverage regardless of site location and access

Digital data facilitates custom image analysis and output

Synergism between data sets


Data Integration(cont'd)

Remotely sensed data can contribute in several forms

Raster image providing continuous detail and an accurate base

Polygon data extracted by classification or visual interpretation

Vector data extracted by enhancement or visual interpretation

Figure 7.8 is an example of multiple data sets integration.


Figure 7.8 GIS Data Integration


Integrating Remotely Sensed Data

Synergism between remotely sensed data acquired at different times, wavelength, resolutions, etc. canincrease the useful information content.

Multi-temporal data are often used to take advantage of seasonal or phenological changes in vegetation and for change detection.

Multi-sensor data can make use of the different information as a function of wavelength, resolution and/or scale.


Integrating Remotely Sensed Data(cont'd)

Multi-channel data can make use of information within different regions of the EM spectrum.

Multi-polarization data can make use of different information in the microwave band related to target interaction with the radar waves.

Polarimetric SAR data can make use of phase as well as magnitude.

Multi-feature data can make use of different information from the same scene, for example, tone and texture.

See Figure 7.9 for an example of SAR and TM integration


Figure 7.9 - Multi-Sensor Combinations


RGB Colour Spaces

Red, green, blue (RGB) colour space is based upon the additive properties of primary colours.

System is optimised for computer video screens but not for human vision.

Multi-channel data is displayed using RGB technique where each channel is assigned a colour with the intensity related to the magnitude of the spectral data.

The RGB colour cube is shown in Figure 7.10.

The colour cube shows the interrelationships between the colours and is defined by the brightness levels of each of red, green and blue.


Figure 7.10 - RGB Colour Spaces

RGBAdapted from: Schowengerdt, 1983

Blue Cyan

White

GreenBlack

Magenta

Red Yellow

Gray Scale


IHS Colour Space

Intensity-Hue-Saturation (IHS) colour space is an alternative way to describe colours by their RGB components, see Figure 7.11.

IHS converts three bands into an alternative colour space closer to what the human eye perceives them.

IHS is more adapted to human vision than the more standard RGB colour space.

The 3 bands can be from different sensors, such as RADARSAT and Landsat TM.


Figure 7.11 - IHS Color Space

Adapted from: Drury, S.A. Image Interpretation in Geology, Second Edition, 1993. Chapman & Hall, p.135.

Cyan180º

Green120º

Yellow60º Red

Whi

teB

lack

Blue

White

INTE

NSI

TY

INTE

NSI

TY

Black

No colour Full colour

IHS

HUE

SATURATION

Magenta300º

Intensity - Hue - Saturation


IHS Colour Space(cont’d)

Intensity is the colour brightness, hue is the actual colour and saturation defines the purity or "greyness" of the colour.

A common approach is to modulate the intensity channel using a SAR image, with other data (geophysics, geochemistry, visible/infra-red image) modulating hue and a flat image replacing saturation.

IHS can improve image sharpness and edge extraction.

The IHS image in Figure 7.12 modulates the intensity and the hue channels using a SAR image and a DEM.


Figure 7.12 - IHS Transform


Principal Component Analysis

Requires multi-channel data set (e.g. multi-date, multi-polarization, multi-sensor).

Can be used to identify new axes that maximize variance in the data set (see Figure 7.13).

Reduces the dimensionality of the multi-channel input to the dimension of the information content.

Any features or patterns identified on a PCA should be confirmed through interpretation of supporting image products and other more conventional data sets.

Eigen Vectors (or principal component channels) usually do not transfer well between data sets.


Figure 7.13 - Axes Rotation Along the Principal Component Vectors

Source: CCRS

Composante 2

FREQUENCYHISTROGRAMS

SCATTERPLOT

B1 VS B2

Band 1

IDENTIFY NEW AXES WHICHMAXIMIZE VARIANCE IN THE DATA SET.

Component 1

E´´

Band 2

BAND 1

BAND 2

E´

B1 E´´

B2 E´Rotation of axes

FREQUENCYHISTROGRAMS


SAR SystemsandDigital Signal Processing


What is Synthetic Aperture Radar (SAR)?

A side-looking radar system which makes a high-resolution image of the Earth’s surface (for remote sensing applications)

The basic image is complex-valued and 2-dimensional:

– range = distance from sensor (perpendicular to flight path)

– azimuth = distance along flight path

Digital signal processing is used to focus the image and obtain a higher resolution than achieved by conventional radar systems


Concept of Synthetic ApertureSynthetic Aperture

Distance SAR travelled while objectwas in view = synthetic aperture

Last time SARsenses object

Flightpath

GroundTrack

Swath

First time SARsenses object

Nadir

Object


SAR Real Aperture

The Real Aperture of a SAR is the slant range plane interval of the transmitted pulse for which all signals return to the receiving antenna at the same instant of time.

– All signals at the same range return to the radar at the same time and are separable only in Doppler shift.

– For a transmitted chirp of length τ, the instantaneous radar return at range R contains surface returns corresponding to slant range interval, c τ /2, each uniquely coded in chirp frequency.

– On a smooth Earth, the constant Doppler frequency contours form a family of hyperbolae and the constant range contours form a family of circles.

– The real aperture determines the range of influence of a radar saturation event.


Point Target Echo in a Synthetic Aperture Radar System

AZIMUTH

RANGE POINT TARGET

TRANSMITTEDWAVEFORM

ANTENNA

MOTION DATA RATE = PRF X NUMBER OF RANGE CELLS

POINT TARGETPHASE HISTORY

SPACECRAFT

RANGE

SYNTHETIC APERTURE LENGTH AZIMUTH

DATA RECORDING

CHIRPLENGTH


Airborne SAR Flight Geometry

R1

H = 2 - 10 km

R2

R1 = Minimum slant range

R2 = Maximum slant range

Flight path

Range

Offset = 5 - 100 km

Azimuth

Imaged swath width5 - 30 Km


SAR Squint Angle

RADAR SWATH

SQUINT ANGLE

ZERO DOPPLER

SQUINTDIRECTION

SAR

AZIMUTH ANGLE


Principles of SAR

Radar coherence

SAR System components

SAR signal generation

Coherent demodulation

How demodulation creates phase

Pulse after range compression

Target in computer memory

Sensor motion equations

Azimuth signal analysis

Doppler frequency

Doppler bandwidth

Azimuth resolution

Synthetic aperture concept

SAR signal processing


Radar Coherence

Consider 2 ways the radar can measure echo time delay:– by observing the time delay of the echo magnitude

(e.g. 56 nsec accuracy = 8 m)– by observing the phase of the echo

(e.g. 6 psec relative accuracy = 1 mm)

A coherent radar has the ability to measure phase, achieved through precise control over:– start time and phase angle of the transmitted pulse– frequency of the coherent oscillator (demodulator)– platform motion including motion compensation


Components of a SAR System

To Signal Processor

CoherentDemodulator

High Power Amplifier

CoherentOscillator

A/DConverter

Low NoiseAmplifier

Circulator

Antenna

Tx/Rx

Pulse Generator

The coherent oscillator (coho) is a very stable clock which provides timing for the signal generation, transmission time, sampling window, demodulation and A/D converter


Antennas

An antenna couples electromagnetic waves (signals) propagating in free space to and from a transmission line.– frequency dependent– directional– polarization dependent

For SAR applications the axis that defines the wave’s electric field orientation with respect to the antenna defines the wave polarization. The general case is elliptical polarized waves.An antenna focuses the radiated waves into a beam in three dimensions.– for efficiency the radiating aperture > 1 wavelength– large radiating areas (apertures) can make tight beams– the gain of an antenna is determined by

• electrical losses• beam area (solid angle)


SAR Signal Generation

X

Chirp: Bandwidth = 20 MHz

Transmitted Pulse

ModulatorTo HPA

Tx pulse looks like a sine wave, but is a chirp with low fractional bandwidth

Carrier from coho: Freq = 5.3 GHz


Coherent Demodulation

X

Received Signal

Demodulated Signal

DemodulatorTo ADC

Demodulated signal is just like the original chirp generated

Carrier from coho: Freq = 5.3 GHz


Received Signal

Stored Rx Signal Stored Demodulated signal

Range Time −−−−> Range Time −−−−>

<−−−

− A

zim

uth

Tim

e

30−May−99 12:0 demod_phase.eps

Received Signal

Stored Demodulated SignalStored Rx Signal

Range Time → Range Time →

←

Azim

uth

Tim

e

How Demodulation Turns Time Delay Into Azimuth Phase


SAR Processing 1Once the radar illumination beam has passed over a point on the ground, all of the information from that point has been acquired and stored as a two dimensional (range and azimuth) phase history.– In the absence of radar saturation, all of the phase histories of

all of the points in the image are linearly combined in a time series to form the SAR “signal” data.

– SAR processing decodes the phase signature of each point in range and azimuth and focuses this information into an impulse response. The range and azimuth widths of the impulse response are the range and azimuth resolutions.

– Nyquist’s theorem requires that the processed data be sampled at least twice per impulse response width. These samples are the radar image “pixels”.


SAR Processing 2Because the natural coordinates of the range and azimuth data are not separable, the range and azimuth processing steps are coupled.– Range walk and range curvature

• Resolution vs. beam width• Beam squint (antenna pointing angle βSQ, relative to zero-

Doppler)• Earth rotation

Processing is done in the natural coordinate system of the radar, the slant range plane.– Earth surface presentations of radar images require projection along

constant range arcs to the Earth surface elevation at each point. RADARSAT data are often projected to an ellipsoid model of sea level.

Calibration separates the radar and the gross imaging geometry from the radar data by inverting the radar equation.


Point Target Compression or Focussing

LOOK 1 LOOK 2 LOOK 3 LOOK 4

AZIMUTHCOMPRESSION RATIO

AZIMUTHCOMPRESSION

AZIMUTHRESOLUTION

CHIRPLENGTH

RANGECOMPRESSION

= SINGLE LOOK APERTURE LENGTHAZIMUTH RESOLUTION

SINGLE LOOKAPERTURE LENGTH

RANGEWALK

RANGECOMPRESSION RATIO

RANGERESOLUTION

CHIRP LENGTH

RANGE RESOLUTION

RANGECURVATURE

=


Signal before range compression

Range time −−−−>

Signal after rangecomp


19−May−99 12:39 comp_pulse.m

Signal before range compression Signal after range compression

Range time → Range time →

Range Compression of Received Signal


Point Target in Computer Memory

Real part of demodulated signal

at range R vs. azimuth time

Real part of demod. signal vs. range time

(azimuth time increases with each line)

R

Real part of demodulated signal vsrange time (azimuth time increases with each line)

Real part of demodulated signal at range R vs azimuth time

R


Signal Analysis in the Azimuth Direction

0 2 4 6 8 10 12

−1

−0.5

0

0.5

1

Sig

nal a

mpl

itude

−−

−−

>Case A Radar is stationary with respect to target

0 2 4 6 8 10 12−2

−1.5

−1

−0.5

0

0.5

1

Azimuth sample number −−−−>

Sig

nal a

mpl

itude

−−

−−

>

Case B Target moving away from the radar at a constant rate

Over this time, 2R has decreased by λ

When the azimuth signal is analyzed, a sine wave is observed in Case B as the target is moving.

The sine wave frequency = the TARGET DOPPLER FREQUENCY

Case A Radar is stationary with respect to target

Case B Target moving away from the radar at a constant rate

When the azimuth signal is analyzed, a sine wave is observed in Case B as the target is moving. The sine wave frequency = the TARGET DOPPLER FREQUENCY

Azimuth sample number →

Sign

al a

mpl

itude

→

Sign

al a

mpl

itude

→

Over this time, 2R has decreased by λ


Phase Change Induced by Sensor Motion

Phase vs Time:cycles

m

Range vs Azimuth Time:

( ) ( ) 220

0

2 2R t R Vt tRλ λ λ

φ = − ≅ − −

( )2

20

02VR t R tR

≅ +

m

Platform motion

Radar

Zero-Doppler Point

Target

Range

( ) 22 2 20R t R V t= +

R

0RVt


Doppler Frequency from Phase Change

Hz

Doppler frequency vs. azimuth time: Hz

This is a linear FM signal:

22d

a

d VF tdt R

K t

φλ

−= =

=

Azimuth Time

DopplerFrequency

Slope = Ka Hz/s

Total Doppler Bandwidthof target DBW

Total exposure time of target

Azimuth Time

Total exposure time of target

Total Doppler Bandwidthof target (DBW)

Slope = Ka Hz/s

DopplerFrequency


Total Doppler Bandwidth Generated

- independent of range and wavelength !- the smaller is D, the larger is the DBW !

Length of beam footprint:

Exposure Time:

Total Doppler Band Width:

Antennalength D

Satellitemotion

Azimuth beamwidth α

Length of beam footprint L= synthetic aperture

Range

R

seL RTV D

λ= =

Hz2

a eVDBW K TD

= =

metersRL R

Dλα= =


Azimuth Resolution

Thus the SAR has the remarkable property that its resolution is independent of distance and radar wavelength !

However, the SNR goes down with increasing rangeand increasing frequency, so higher power may be needed at long ranges.

Doppler Bandwidth Hz

therefore resolution in time s

and resolution in space units = resolution in time * V

m

2VD

=

2DV

=

2D=


SAR Signal Processing

Overview of processing algorithms availableStructure of the received SAR signalThe Range/Doppler algorithmRange pulse compressionRange resolution obtainedDoppler centroid estimationRange cell migration correction (RCMC)Azimuth compressionMulti-looking to reduce speckleThe SPECAN algorithm


SAR Processing Algorithms

Range/Doppler– a widely-used general-purpose algorithm– good compromise between accuracy and speed

SPECAN– for quick-look or ScanSAR processing

Chirp Scaling– for the highest phase accuracy and moderate squint

Wave Equation– for systems which operate with wide apertures and/or

large squint anglesPolar Format– for spotlight radar processing


Structure of Transmitted SAR SignalThe transmitted SAR signal is usually a linear FM pulse:

(1)

where η = azimuth time sτ range time sP(τ) envelope of range pulse (chirp)f0 radar carrier frequency HzKr range FM rate Hz/sτl duration of range chirp s

These pulses are repeated at the rate of Fa Hz, which we refer to as the Pulse Repetition Frequency (PRF).

Note that τ is continuous time, while η is a discrete time variable.

( ) ( ) ( ){ } [ ]20, cos 2 / 2 , 0,t r l lS P f Kη τ τ π τ π τ τ τ τ= + − =


Structure of Received SAR Signal

The ideal received signal from a single point target can be expressed as:

The ideal received signal is the same signal as was transmitted, but with a time delay τd proportional to the range R:

where R(η) is the range to the point target for the pulse transmitted at time η and c is the speed of light.

( ) { } ( ) ( ){ }[ ] ( )

20, cos 2 / 2 ,

, 2r d d r l d

d l d

S P f Kη τ τ τ π τ τ π τ τ τ

τ τ τ τ

= − − + − −

= −

( ) ( )2 / 3d R cτ η=


The Range EquationThe most important geometry relationship is given by the range equation:

which comes from the right-angled triangle with sides R0 and Vr (η - η 0 )and hypotenuse R(η), where the straight-line platform motion approximation is made. As Vr (η - η 0 ) << R0 we can use a Taylor series to approximate R(η) by the parabola:

( ) ( )22 220 0rR R Vη η η= + −

( ) ( ) ( )220 0 0/ 2rR R V Rη η η= + −

Target

Range

R0

Platform motionRadar position

Zero-Doppler Point

( )0rV η η−R (η)


Structure of Demodulated SAR Signal

After coherent demodulation, the signal from the point target can be expressed as:

where we have included A, the azimuth beam profile (gain) which is a function of the time from the beam centrecrossing time ηc.

( ) ( ) ( )( ){ }

[ ] ( )

20

,

exp 2 / 2 ,

, 4

d c d

d r l d

d l d

S A P

j f j K

η τ η η τ τ

π τ π τ τ τ

τ τ τ τ

= − −

− + − −

= +


SAR Data Acquisition

Flight path

SAR Signal Memory

A

B

Nadir

Azimuth

Range

Target

SAR

R(ηA)

R(ηB)

Beam along surface

SAR Signal Memory

Range

Ground Track

Azimuth

SAR

Flight path

Beam along surfaceTarget

R (ηB)

R (ηA)

A

B

Nadir


Received Data in SAR Signal Memory

When the echo from each pulse is received, it is written into one line in SAR signal memory (along constant azimuth time).As the platform (or target) moves, the echo from a given target shifts in range, and is written into the next range line in the memory (going up the slide).After the beam has finished illuminating the target, the locus of energy has the shape shown in red.The purpose of SAR signal processing is to compress this energy into a single point.

0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

45

Slant Range (cells) −−−−>

Azi

mut

h (

cells

) −

−−

−>

Locus of point target energy in signal memory

η0

ηc

start of target exposure

end

Slant Range (cells) →

Locus of point target energy in signal memory

Azi

mut

h (c

ells

)

→

start of target exposure

η0

ηC

end


Simulation ParametersSize of azimuth array Na 256 complex samples Size of range array Nr 128 complex samples No. of samples in chirp 104 complex samples No. non-zero range lines 239 complex samples Duration of chirp τ l 5.20 µsecRange FM rate Kr 3.27 MHz / µsecRange sampling rate Fr 20.0 MHzRange bandwidth 17.0 MHz

Radar wavelength λ 1.036 cm Speed of wave prop. c 300.0 Km/msec Range of target R0 850 KmPRF Fa 1700 HzTotal Doppler bandwidth 1410 HzPlatform Velocity Vr 7050 m/s

Azimuth FM rate Ka -11289 Hz/s"PRF" duration 150.59 msecBeam offset ηc -6.34 sDoppler centroid Fcen 71613 HzDoppler centroid 42.125 PRFsDoppler centroid Ffrac 213 HzAntenna length D 10.0 mActual RCM 6.92 cells


Energy of Range Signal

020

4060

80100

120

0

50

100

150

200

250

0

0.2

0.4

0.6

0.8

geninp2.epsRange −−−−>

Envelope of Received SAR Signal etac = −6.34 s rcm = 6.92 cells

<−−−− Azimuth

Mag

nitu

de −

−−

−>

16−May−99 13:51

Envelope of Received SAR Signal ηc = -6.34 s RCM = 6.92 cells

←

Azimuth

Range →

Mag

nitu

de

→


The Range/Doppler AlgorithmSARSignalData

MLDIMAGE

SLC Image

UnpackEncodedData

BalanceI & Q

Channels

RangeCompression

AzimuthFFT

DopplerCentroidEstimation

Range CellMigrationCorrection

MatchedFilter

Multiply

Detection,Look Summation

LookExtraction,

Azimuth IFFT


Range Processing

Generate range matched filter– Get replica of ideal range pulse– Reverse sequence in time– FFT the sequence with zero padding– Conjugate the answer– Apply smoothing window

FFT each range lineMultiply by range matched filterInverse FFTSelect good output points


Range Matched Filter

−60 −40 −20 0 20 40 60

−350

−300

−250

−200

−150

−100

−50

0

Spectrum of signal in range line 128

Range frequency (bin no.) −−−−>

Pha

se (

radi

ans)

−−

−−

>

−60 −40 −20 0 20 40 60

0

50

100

150

200

250

300

350

Spectrum of matched filter


Pha

se (

radi

ans)

−−

−−

>

13−May−99 12:42 rangemf2.eps

Phas

e (r

adia

ns)

→

Spectrum of signal in range line 128

Spectrum of matched filter

Phas

e (r

adia

ns)

→

Range frequency (bin no.) →

Range frequency (bin no.) →−60 −40 −20 0 20 40 60

0

2

4

6

8

10

12

14

Spectrum of signal in range line 128 (fftshifted)


Mag

nitu

de −

−−

−>

−60 −40 −20 0 20 40 600

2

4

6

8

10

12

14

Spectrum of range MF, with & without window


Mag

nitu

de −

−−

−>

13−May−99 12:42 rangemf1.eps


Mag

nitu

de

→

Spectrum of signal in range line 128 (FFT shifted)

Spectrum of range MF, with & without window


Mag

nitu

de

→


Range Pulse Compression

Signal before range compression


Signal after rangecomp


19−May−99 12:39 comp_pulse.m

Signal before range compression Signal after range compression

Range time → Range time →


Range Compression Results 1

5055

6065

7075

0

50

100

150

200

250

0

20

40

60

80

rangcom2.epsRange −−−−>

Signal after range compression etac = −6.34 s RCM = 6.92 cells


Mag

nitu

de −

−−

−>

19−May−99 13:4

←

Azimuth

Range →

Mag

nitu

de

→

Signal after range compression ηc = - 6.34 s RCM = 6.92 cells

0 20 40 60 80 100 120

0

50

100

150

200

250

Range compressed signal

Range cell no. −−−−>

Azi

mut

h ce

ll no

. −

−−

−>

19−May−99 13:4 rangcom1.epsRange cell no. →

Azi

mut

h ce

ll no

. →

Range compressed signal

Az i

mut

h ce

l l no

.

→



The data is now range compressed, but a significant range migration remains.

50 55 60 65 70 75

50

100

150

200

250

Range cell number −−−−>

Azi

mut

h ce

ll nu

mbe

r −

−−

−>

Contour plot of magnitude of range compressed signal

19−May−99 16:18 contour4.eps

Azi

mut

h c e

ll n u

mbe

r

→

Range cell number →

Contour plot of magnitude of range compressed signal


Range Resolution

The slant range -3 dB resolution in seconds is equal to:

where BWr is the bandwidth of the range pulse

A weighting function is used in the matched filter to control the range sidelobes, and leads to the weighting factor Qr (typically 1.2)ρsr is multiplied by half the speed of light to get the slant range resolution in metresρsr is also divided by sin(θ ) to get the ground rangeresolution in metres:

rsr

r

QBW

ρ = s

( )( )

sinrgr

r

Qc BW

θρ = m



54 56 58 60 62 64 66 68−35

−30

−25

−20

−15

−10

−5

0Compressed pulse in range line 128

Time (samples expanded by 16) −−−−−>

Mag

nitu

de (

dB)

−−

−−

>

Pkindex

= 60.88 samples

Pkvalue

= 80 units

Pkphase

= 0.0 deg

Resolution = 1.189 cells

Maxlobe

= −18.0 dB

1D ISLR = −14.9 dB

15−May−99 12:57 pulse3.ep

Compressed pulse in range line 128

Time (samples expanded by 16) →

Pkindex = 60.88 samples

Pkvalue = 80 units

Pkphase = 0.0 deg


Maxlobe = -18.0 dB

1-D ISLR = -14.9 dB

Mag

nitu

de (d

B)

→



54 56 58 60 62 64 66 68−200

−150

−100

−50

0

50

100

150

200Compressed pulse in range line 128


Pha

se A

ngle

(de

g) −

−−

−>

15−May−99 12:57 pulse4.eps

Compressed pulse in range line 128


Phas

e An

gle

(deg

)

→


Azimuth FFT 1

Mag

nitu

de

→

4550

5560

6570

75

0

50

100

150

200

250

0

200

400

600

800

1000

1200

azfreqdm.epsRange position (cells) −−−−>

Signal magnitude after azimuth FFT

<−−−− Azimuth frequency (cells)

Mag

nitu

de −

−−

−>

15−May−99 13:27

Signal magnitude after azimuth FFT

Range position (cells) →

←

Azimuth frequency (cells)

Mag

nitu

de

→


Azimuth FFT 2

The azimuth FFT causes a circular rotation of the data around the azimuth axis, because of the conversion from time to frequency.

50 55 60 65 70 75

0

50

100

150

200

250

Contour plot of signal energy after the azimuth FFT

Range position (cells) −−−−>

Azi

mut

h fr

eque

ncy

(cel

ls)

−−

−−

>

19−May−99 16:18 contour2.epsRange position (cells) →

Contour plot of signal energy after the azimuth FFT

Azi

mut

h fr

eque

ncy

(cel

ls)

→


Doppler Centroid Estimation

The centre of the azimuth or Doppler energy is a function of the antenna squint angle and the Earth rotation and must be estimated now, as it is needed for RCMC and azimuth compressionThere are many ways of estimating the Doppler Centroid, e.g.:

– Curve-fitting the azimuth magnitude spectrum– Estimating the average phase increment– Beating two range looks together

The Doppler centroid is ambiguous, as the energy is aliased to the interval ( 0 : Fa ). Both the aliased centroid and the ambiguity number must be estimated.


Aliasing of the Doppler Spectrum

>

>

0 Fa M Fa (M+1) Fa

Azimuth frequency (Hz) −−−−>

Dop

pler

ene

rgy

Measured spectrum True spectrum

15−May−99 14:59 amb_illus.eps

Dop

p le r

ene

rgy

Measured spectrum

Azimuth frequency (Hz) →

Fa M Fa (M+1) Fa

True spectrum

0

*true meas aF F M F= +

If the Doppler energy could be observed as an analog signal, the red spectrum would be seen.But, as the Doppler spectrum is sampled at a rate of Fa Hz, the spectrum is aliased down to the interval (0 :Fa) as shown in blue. This blue spectrum is all we can observewith the sampled data.M is referred to as the ambiguity number.We must estimate M as it is needed for range cell migration correction.


The Doppler Ambiguity Number

>

>

0 Fa M Fa (M+1) Fa

Azimuth frequency (Hz) −−−−>

Dop

pler

ene

rgy

Observed spectrum True spectrum

>

Ffrac

>

Fcen

15−May−99 16:6 amb_illus2.eps

Dop

p le r

ene

rgy

Azimuth frequency (Hz) →

M Fa (M+1) Fa

True spectrumObserved spectrum

Fa

FfracFcen

0

*cen frac aF F M F= +

In general, the Doppler energy is not between integer Faboundaries.

The total or absolute Doppler centroid is Fcen

The observed Doppler centroid is Ffrac

In addition to Ffrac, we need to estimate the Doppler ambiguity number M, so that we can obtain:


Average Phase Method

−1.5 −1 −0.5 0 0.5 1 1.5−1.5

−1

−0.5

0

0.5

1

1.5Estimated F

frac = 211 Hz

Real part −−−−>

Imag

par

t −

−−

−>

Azimuth phase increments in DC range frequency cell

19−May−99 14:19 accc.epsReal part

Estimation of the Doppler Centroid by the average azimuth phase vectors method

Real part →

Imag

ina r

y pa

r t

→

Estimated Ffrac = 211 Hz


Finding the Doppler Ambiguity

−60 −40 −20 0 20 40 60

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

etac = −6.344 s

squint = −3.0 deg

Ffractrue

= 213 Hz

Ffracest

= 212 Hz

Range frequency (bins) −−−−>

AC

CC

ang

le (

radi

ans)

−−−

−>

DLR algorithm: ACCC angle vs. range frequency (fftshifted)

Fit Error = 13.71 mrads

Cubic Err = 0.065 mrads

Slope = 9.192 mrad/MHz

Fcentrue

= 42.13 PRFs

Fcenest

= 42.18 PRFs

19−May−99 14:39 dopcen1.eps

DLR algorithm: ACCC angle vs. range frequency (FFT shifted)

Range frequency (bins) →

AC

CC

an g

le (r

adia

n s)

→ηc = -6.344 sSquint angle = -3.0 degFfrac = 212 Hz

est

Ffrac = 213 Hztrue

Fit Error = 13.71 mradsCubic Err = 0.065 mradsSlope = 9.192 mrad/MHzFcen = 42.13 PRFs

trueFcen = 42.18 PRFs

est


Range Cell Migration Correction

0 1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12Total Range Migration vs. Beam Squint

Beam centre offset magnitude |etac| (s) −−−−>

Tot

al R

CM

(ra

nge

cells

) −

−−

−>

Simulation value

Target exposure = 0.141 s

19−May−99 14:45 RCMtot.eps

T ota

l RC

M ( r

ange

ce l

ls)

→

Simulation value

Target exposure = 0.141 s

Total Range Migration vs Beam Squint

Beam centre offset magnitude | c| (s) →

range cells

The total range migration comes from the range equation. When expressed in range cells, we can determine when RCM correction is needed:

2

0

2 r rl c

V FRCMc R

η η=

Beam centre offset magnitude |ηc| (s) →


Azimuth frequency index0 50 100 150 200 250

70.8

71

71.2

71.4

71.6

71.8

72

72.2

72.4

Frequency vector for RCMC calculations

Azimuth frequency index −−−−>

Una

liase

d or

abs

olut

e fr

eque

ncy

(K

Hz)

−−

−−

>

DOPCEN = 71.61 KHz M = 42

19−May−99 14:52 favector.eps

0

1

2

3

4

5

6

7

RC

M n

eede

d (

rang

e ce

lls)

−−

−−

>

Frequency vector for RCMC calculations

Azimuth frequency index →

DOPCEN = 71.61 KHzM = 42

Una

liase

dor

abs

olut

e fr

eque

ncy

(KH

z)

→

RCM

need

ed (r

ange

cel

ls)

→

RCM Calculation1. Compute absolute frequency of each frequency sample2. Compute RCM needed in range cells:

( )2

2028 r

RR f fV

λ=


Coefficients of filter for interpolating 1/16 of a cell

Shift amount (1/16 cell) →

Coe

ffici

ent v

alue

→

Before weighting After weighting

RCMC Interpolator Design 1

To perform RCMC, we need an interpolator.We design one based on a weighted sinc function.


1 2 3 4 5 6 7 8

−0.2

0

0.2

0.4

0.6

0.8

1

16 sets of 8−point interpolators designed with Kaiser window, beta = 3

Coefficient number

Coe

ffici

ent v

alue

−−

−−

>

Only sets 1:8 are shown(sets 9:15 are symmetrical)(set 16 is the no−shift set)

17−May−99 16:29 fildes2.eps

16 sets of 8-point interpolators designed with Kaiser window, β = 3

Coefficient number

Only sets 1:8 are shown(sets 9:15 are symmetrical)(set 16 is the no-shift set)

Coe

ffici

ent v

alue

→

RCMC Interpolator Design 2The red curve of the previous slide is sub sampled, with an 1/16 cell shift to get the individual coefficient sets:


RCMC Results 1

0 50 100 150 200 2500

2

4

6

8

10

(a) Amount of RCMC needed

Azimuth frequency (bin no.) −−−−>

Ran

ge (

cells

) −

−−

−>

Total RCMCInteger RCMCFract RCMC

55 60 65 70 75 800

2

4

6

(b) Energy of target before RCMC

Range (cells) −−−−>

Mag

nitu

de −

−−

−>

17−May−99 17:4

45 50 55 60 65 700

2

4

6

(c) Energy of target after integer RCMC


Mag

nitu

de −

−−

−>

45 50 55 60 65 700

2

4

6

(d) Energy of target after total RCMC


Mag

nitu

de −

−−

−>

rcmc1.eps

(a) Amount of RCMC needed (c) Energy of target after integer RCMC

(b) Energy of target before RCMC (d) Energy of target after total RCMC

Range (cells) →

Azimuth frequency (bin no.) →

Mag

nit u

de (c

e ll s

) →

Mag

nit u

de (c

e ll s

) →

Mag

nit u

de (c

e ll s

) →

Range (cells) →

Range (cells) →

Ra n

ge (c

e lls

) →

Total RCMCInteger RCMCFract RCMC


RCMC Results 2

4550

5560

6570

0

50

100

150

200

250

0

200

400

600

800

1000

1200

rcmc2.epsRange position (cells)

Signal magnitude after RCMC (every 12th line is shown)


Mag

nitu

de −

−−

−>

17−May−99 16:54

Signal magnitude after RCMC (every 12th line is shown)


Mag

nitu

de

→

Range position (cells)


RCMC Results 3

0 20 40 60 80 100 120

0

200

400

600

800

1000

Mean Square energy of RCMCed signal vs. range


MS

Ene

rgy

−−

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Blowup of graph above


MS

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19−May−99 15:1 rcmc3.eps

Range cell no. →

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gy

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Mean Square energy of RCMCed signal vs range

Blowup of graph above

Range cell no. →

MS

Ener

gy

→


45 50 55 60 65 70

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Contour plot of signal energy after RCMC

Range position (cells) −−−−>

Azi

mut

h fr

eque

ncy

(cel

ls)

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>

19−May−99 16:15 contour3.eps

Range position (cells) →

Azi

mut

h fr

e que

ncy

(cel

ls)

→

Contour plot of signal energy after RCMC

RCMC Results 4

The data is now well-aligned in the azimuth direction --the data lies mainly in one range cell.


Azimuth Compression

After RCMC, the azimuth energy is aligned vertically in the computer memoryAzimuth compression consists of:– generation of matched filter– look extraction, with weighting– inverse discrete Fourier transform (DFT)

The azimuth matched filter parameters are computed from the azimuth FM rate, the exposure time and the Doppler centroidThe azimuth matched filter is also a linear FM signal, and is applied with a fast convolution, much like the range compression operation.


Azimuth Matched Filter

To derive the matched filter: – generate replica of ideal received signal– reverse it in time– zero pad, and take its DFT

To apply the matched filter:– select portion of azimuth spectrum to utilize– multiply by window and matched filter– inverse DFT– select good output points


Azimuth Signal Properties

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Sig

nal m

agni

tude

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Slice of signal data down range cell 57 (max energy)

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le (

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18−May−99 10:34 azimmf1.epsAzimuth frequency cell →

Ang

le (r

adia

ns)

→Si

gnal

mag

nitu

de

→

Azimuth frequency cell →

Slice of signal data down range cell 57 (max energy)


4550

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6570

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azcomp2.eps

Range −−−−>

Compressed data after azimuth processing


Mag

nitu

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18−May−99 11:13

Compressed data after azimuth processing

Range →

Mag

nitu

de

→

←

Azimuth

Form of the Compressed Pulse After Azimuth Compression


Azimuth Compression

Results 2

Blue curve:-data summed in azimuth

Red curve:-data summed in range

0 20 40 60 80 100 120

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mut

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1−D integrations over range and azimuth

18−May−99 11:13 azcomp1.eps

Range cell no. →

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mpl

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. →

1-D integrations over range and azimuth


Azimuth Compression Results 3

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2D expansion of compressed pulse

Range (samples expanded by 4) −−−−−>

Azi

mut

h (

sam

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ande

d by

4)

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−>

Peakmag

= 14748

Pkr−indx

= 57.25

Pka−indx

= 36.00

Pkphase

= −1.8

19−May−99 16:45 contour5.epsRange (samples expanded by 4) →

Az i

mu t

h ( s

a mp l

e s e

x pa n

d ed

b y 4

)

→

2D expansion of compressed pulse

Pkmag = 14748

Pkr-index = 57.25

Pka-index = 36.00

Pkphase = -1.8


AzComp Results -- Azimuth Slice

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= 14748 units

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= −1.8 deg


Maxlobe

= −18.0 dB

1D ISLR = −16.3 dB

18−May−99 18:59 pulse3.epsTime (samples expanded by 16) →


P has

e an

g le

(de g

) →

Ma g

n it u

d e ( d

B)

→


Maxlobe = -18.0 dB

1D ISLR = -16.3 dB

Pkindex = 36.00 samplesPkvalue = 14748 unitsPkphase = -1.8 deg


AzComp Results -- Range Slice

40 50 60 70 80 90−200

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g) −

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Mag

nitu

de (

dB)

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>

Pkindex

= 57.13 samples

Pkvalue

= 14893 units

Pkphase

= −1.8 deg


Maxlobe

= −18.1 dB

1D ISLR = −15.0 dB


P has

e an

g le

(de g

) →

Mag

n itu

d e (d

B)

→



Maxlobe = -18.1 dB

1D ISLR = -15.0 dB

Pkindex = 57.13 samplesPkvalue = 14893 unitsPkphase = -1.8 deg



Multi-Looking ConceptSingle look image uses all signal returns from a ground target to create a single image. The image will contain speckle but have the highest achievable resolutionMulti looking is used to reduce speckle in the final detected image, assuming that phase is not needed.

Independent images of the same area can be formed in the digital processing of SAR data by using sub-sets of the signal returns. Achieved by compressing subsets of the azimuth signal energy (spectrum) independently, and adding their detected images together after registration.In satellite SARs, 3 or 4 looks are typically taken, with the azimuth resolution and number of looks selected to make the azimuth pixel size approximately equal to the ground range pixel size.

Resulting image has lower resolution but reduced speckle


The SPECAN Algorithm

Optimal for low resolution, multi-look or ScanSAR processing

Following conventional range compression, azimuth compression is achieved by a matched filter multiply followed by an azimuth FFT

There is no azimuth IFFT, so the algorithm is very efficient

This saving is possible because of the linear FM structure of the received signal

http://www.ee.ubc.ca/sar/sqlp/sqlp.html


SummaryIllustrated SAR compression with the R/D algorithm

– Obtained well-focussed results

– Carefully-designed matched filters with weighting

– RCMC done correctly

– Doppler parameters estimated accurately

Other algorithms available for specialized purposes

– SPECAN

– Chirp scaling

– Wave Equation

– Polar Format

Advanced Topics - SAR Systems and Digital Signal Processing

Notes

Slide 2

A SAR system, as used in remote sensing, has two features which distinguish it from other radar systems:

• It makes a 2-dimensional image by having the radar platform move in a straight line during the data collection. The second dimension is given by measuring the time delay of the received radar pulse.

• It obtains high resolution in the motion direction by focussing or compressing the Doppler energy arising from the platform motion.

As the radar is a coherent system (preserving phase), it is convenient to perform the signal processing using complex numbers. Also, the pulse repetition frequency (PRF) is kept low to obtain large swath widths, so complex numbers are needed to properly sample the received signal.

In the early days of SAR, users were only interested in the magnitude of the processed image, but now they are also very interested in the phase. So the final processed image is usually stored in the form of complex numbers.

One of the features that distinguishes a modern radar system from its predecessors is digital signal processing (DSP). With digital processing, focussing can be precise, and image quality maintained at a high level.

Slide 3

What does aperture mean? (Courtesy of the Alaska SAR Facility)

Many people associate the word aperture with photography, where the term represents the diameter of the lens' opening. The camera's aperture then determines the area through which light is collected. Similarly, a radar antenna's length partially specifies the area through which it collects radar signals. The antenna's length is therefore also called its aperture.

Remember, light and radar just represent different wavelengths of electromagnetic radiation, so many terms and equations used in everyday optics also apply in radar theory.

So what does synthetic aperture mean?

In general the larger the antenna, the more unique information you can obtain about a particular viewed object. With more information, you can create a better image of that object (improved resolution). It's prohibitively expensive to place very large radar antennas in space, however, so researchers found another way to obtain fine resolution: they use the spacecraft's motion and advanced signal processing techniques to simulate a larger antenna.

A SAR antenna transmits radar pulses very rapidly. In fact, the SAR is generally able to transmit several hundred pulses while its parent spacecraft passes over a particular object. Many backscattered radar responses are therefore obtained for that object. After intensive signal processing, all of those responses can be manipulated such that the resulting image looks like the data were obtained from a big, stationary antenna. The synthetic aperture in this case, therefore, is the distance travelled by the spacecraft while the radar antenna collected information about the object.

The ERS-1 satellite's SAR sends out around 1700 pulses a second, collects about a thousand backscattered responses from a single object while passing overhead, and the resulting processed image has a resolution near 30 meters. The spacecraft travels around 4 kilometers while an object is "within sight" of the radar, implying that ERS-1's 10 meter x 1 meter radar antenna synthesizes a 4 kilometer-long stationary antenna!

Page 1 of 15Advanced Topics Notes - Radarsystems

Slide 6

This slide showing a SAR system operated from an aircraft illustrates the 2-dimensional nature of the SAR imaging mechanism.

One dimension is the aircraft flight direction, which is called azimuth. The other dimension is given by the radar beam, which is approximately perpendicular to the flight direction. This second dimension is called range, as it is proportional to the range R from the sensor to the reflectors on the ground.

Slide 8

In this group of slides, we will discuss the technical features of SAR systems which allow them to obtain their high resolution in azimuth. Key to this is the concept of coherence, and how the radar signals are timed and processed to maintain and take advantage of the coherence property.

Slide 9

If we can only observe the magnitude of a signal, the best that we can measure is the time of the signal’s reception. The accuracy of this measurement is given by the inverse of the bandwidth of the received signal, e.g. if the bandwidth is 18 MHz, then the time of arrival of a pulse can be measured to an accuracy of 56 nanoseconds. This corresponds to a distance of 8 m.

However, if we can observe the phase to an accuracy of 12o, then (at C-band) the time can be measured to an accuracy of 6 picoseconds, or 1 mm. A coherent radar, with precise control over the frequency of the coherent oscillator, and precise control over the timing of the transmitted pulses, can achieve this higher accuracy.

In the case of an airborne SAR, the platform may not fly in a straight line, because of atmospheric turbulence. When this happens, the received signal must be motion compensated so that the phase of the received signal is the same as it would be if the aircraft did fly in a straight line.

Slide 10

These are the main components of the analogue or radio frequency (RF) parts of a SAR system.

The coherent oscillator generates a very stable frequency, and counters are used to generate the discrete times of pulse generation and analogue-to-digital (A/D) conversion.

The pulse generator generates a chirp signal at low frequency with the desired bandwidth, say 20 MHz. Then the chirp is multiplied by the coherent oscillator to raise its centre frequency to the desired radar frequency, e.g. 5.3 GHz.

This weak RF signal is then amplified to a power of several kW, and fed to the antenna via the circulator. The circulator is a switch which cycles the path to the antenna between the transmit side (Tx) and the receiver side (Rx) of the radar system.

The transmit cycle lasts approximately 30 µsec, while the receive cycle lasts approximately 600 µsec. The circulator also plays the important function of protecting the sensitive receiver from the high power of the transmitter.

The antenna receives the weak echo from the Earth’s surface, and the Low Noise Amplifier (LNA) amplifies it by about 120 dB so that the subsequent analogue and digital electronics can deal with it. Because the LNA has to deal with such a weak received signal, it must have a very low thermal noise figure, to keep the received signal-to-noise ratio (SNR) at a reasonable level.

The demodulator down-converts the signal to baseband (or to an intermediate frequency) so that the sampler can operate at the Nyquist rate for the signal’s bandwidth.


Slide 12

The first step in the SAR signal generation process is to generate a chirp signal with the desired bandwidth, such as 20 MHz. The time of the beginning of the chirp is precisely controlled by a counter running off the coherent oscillator (coho). The beginning of the pulses are separated by the pulse repetition interval, or 1/PRF. Each pulse has exactly the same waveform including the same initial phase.

The pulse is then multiplied by the radar carrier frequency so that the resulting signal has the desired centre frequency, e.g. 5.3 GHz. The carrier is the same as the coho, or is derived from it.

The signal out of the multiplier is filtered so only the signal around the carrier frequency is kept. The signal remaining is then the pulse which is sent to the high power amplifier and transmitted.

The coho signal is a sine wave, and the transmitted pulse also looks like a sine wave, as its fractional bandwidth is very small, e.g. 0.3 %.

Slide 13

The coherent demodulator is essentially the reverse of the up-converter in the signal generator. If the received signal is the same as the transmitted signal (except for a gain change and a time delay), the demodulated signal is the baseband chirp originally generated.

However, the demodulated signal has two important properties:

• it has a time delay given by the return flight time of the signal, and

• it has a phase change proportional to the time delay.

Slide 14

This slide shows how the demodulation process imparts a phase change on the received pulse, proportional to the time delay of the pulse.

The received signal is shown along the top of the slide. In this case, we assume that it is the ideal signal from a point reflector, and the radar and reflector are moving away from each other slowly.

This is more clearly seen by the signals in the lower left panel, where the received signal is chopped up and stored in memory. The memory is 2-dimensional, with each new row of memory beginning at a precise time after the initiation of each transmitted pulse (referred to as range time). The time delay can be seen with respect to the vertical dashed line, which represents a fixed range time. Note that except for the time delay, the received signal has exactly the same shape (phase) in each row. The vertical dimension represents azimuth in this 2-D memory.

However, when the signal is demodulated, the phase of the pulse is changed by the time delay, because the phase of the demodulated signal equals the phase of the received signal minus the phase of the coho. But as the received signal is delayed with respect to the coho, a phase change proportional to delay is imparted on the signal.

The phase change can be observed in the lower right panel, where the circles represent samples taken at a common range time.

Slide 18

After demodulation, the signal is sampled and compressed in the range direction.

The compression is achieved by a matched filter, which is the complex conjugate of the ideal received signal. Weighting


is used to control the sidelobes of the compressed pulse.

The -3 dB width of the compressed pulse (in time units) is approximately equal to the inverse of the bandwidth of the pulse.

The phase of the compressed pulse is equal to the phase of the demodulated signal (at a certain reference point from its beginning).

Slide 19

This slide shows how a range-compressed target appears in signal memory (left panel), where 25 range lines are shown. In the memory, range runs horizontally, while azimuth runs vertically.

The range of the point target is increasing linearly with each pulse (with each range line), but each succeeding time delay increment is so small that the time delay is not obvious in the figure (the total time delay over the 25 pulses is only 93 nsec, representing a λ/2 change in range, or only 0.0019 of a sample).

If we then examine the stored signal at a fixed range R (at the peak of the compressed point target), and draw these 25 samples vs. azimuth time, we observe the sine wave shown in the right panel. This signal is the azimuth signal of the SAR system.

Slide 20

Let us observe the azimuth signal for two cases.

In case A, the target is stationary with respect to the radar. Then there is no differential time delay between the pulses, and the phase of each succeeding pulse is constant. In other words, the azimuth signal shown in the top panel has zero frequency.

Then consider case B, where the target is moving away from the radar at a constant rate, as in the previous slide. Every time the range to the target increases by λ/2 (the transmit plus receive range increases by λ), the azimuth phase changes by 360o, as seen in the lower panel.

The azimuth signal in case B is a sine wave. The frequency of this sine wave is

and is referred to as the Doppler frequency of the target.

Slide 21

This slide shows how the range to a target changes with time as the radar passes by, and the form of the resulting phase change.

Assuming constant-speed, straight-line motion, the zero-Doppler position of the radar, the current position of the radar and the target form a right-angled triangle. The zero-Doppler position is the point where the radar is closest to the target, a distance Ro away.

Then the range R varies with time as a hyperbola, but the hyperbola can be well approximated by a parabola, as the radar beamwidth is relatively narrow.

The change in range induces a phase change, discussed on the previous slide, which also has a parabolic form with time. Note that a signal with a parabolic phase or a linear frequency is a chirp. The form is much like the range chirp, but at a quite different time scale (the azimuth bandwidth is only a few hundred to a thousand Hz).


Note that we have used the units of cycles for phase, so when we differentiate phase relative to time on the next slide, we will get frequency in Hz.

Slide 22

The Doppler frequency is the rate of change of phase, which makes it a linear function of time for the rectilinear SAR motion shown in the previous slide.

The graph shows a typical plot of Doppler frequency vs. time in the linear FM SAR signal of a point target.

The most interesting property of this frequency is the slope of the graph, or the frequency modulation or FM rate, Ka. From the range equation developed on the last slide, we see that the azimuth FM rate is

Other interesting parameters of the signal are its bandwidth, centre frequency and duration or exposure time.

Slide 23

This slide shows the total Doppler bandwidth generated by the SAR system.

The SAR system design gives the fixed SAR parameters of antenna length D, radar wavelength λ and sensor velocity V. The length of the beam footprint and the associated azimuth exposure time are proportional to the range R.

The azimuth FM rate Ka is inversely proportional to range, with the interesting result that the total azimuth bandwidth generated 2V/D is independent of range and wavelength.

In order to make the bandwidth larger (and the resolution finer), the antenna length must be made shorter !

Slide 24

As in other instruments, the resolution, when expressed in time units, is approximately equal to the inverse of the bandwidth, or D/(2V) seconds in this case.

Then to get the resolution in space units, we multiply by the (azimuth) velocity of the sensor, or V. Thus the azimuth resolution is D/2 m.

Slide 25

Digital signal processing of received SAR data is the key to the higher performance of modern radar systems. Originally, SAR processing was performed with coherent laser optics, but in the 1980s, digital processing took over. Digital processing offered the advantage of higher dynamic range, better noise control and more precise focussing. Digital SAR processors were relatively slow at first, but now they can be built to operate in real time.

In this set of slides, we will review the mainstream algorithms in use today, and go through the steps of the most common algorithm, the Range/Doppler algorithm.

Slide 26

These are the main SAR processing algorithms in use for satellite SAR processing today. The Range/Doppler algorithm was developed in 1978, is the most general one, and is the one most widely used. It will handle most SAR cases efficiently, except those with very wide apertures, high squint and ScanSAR.


SPECAN is an algorithm developed in 1979 to use the minimum memory and computing operations for spaceborne use. It turns out to be very efficient for low resolution, multi-look processing, as well as ScanSAR processing. It is particularly efficient for ScanSAR because the time-frequency structure of the SAR processing algorithm can be exactly matched to the time-frequency structure of the ScanSAR data collection. It does not handle range cell migration correction (RCMC) easily.

The chirp scaling algorithm was developed in 1992. Its main advantage is that it obtains higher phase accuracy because it dispenses with the RCMC interpolator. Instead, it performs RCMC by scaling (expanding and shifting in range) the chirp in the range-time, azimuth-frequency domain.

The wave equation algorithm was originally developed for seismic processing, and was adapted to SAR processing in 1986. It is also called the Range Migration Algorithm (RMA), or the Wave Number algorithm. It operates in the 2-dimensional frequency (wave number) domain, and handles wide-aperture and high-squint SAR data accurately, as long as the radar velocity does not vary with range too much. It does not need an explicit Secondary Range Compression term, as this SRC term is implicit in the formulation, but it cannot adjust the SRC term with range.

The polar format algorithm was developed for squinted and spotlight aircraft SARs, and has limited use for satellite SARs. It can focus accurately at any squint angle, but has a limited depth of focus.

Slide 27

The signal is a linear FM pulse imposed upon a carrier frequency of f0 Hz. For ERS, Envisat and RADARSAT, the carrier frequency is C-band at 5.3 GHz.

The linear FM pulse or chirp has the properties of:

• duration τl usually 30 - 40 µs

• centre frequency, usually zero so that f0 is the centre frequency

• bandwidth BW, usually 10 - 30 MHz

• FM rate = BW / τl, often about 0.5 MHz/µs

The pulse is selected to be linear FM so that all frequencies within the selected bandwidth are used equally, a criteria for good pulse compression.

Slide 28

Here we assume that the ground is completely non-reflective except for a single, ideal point target or reflector. This is the easiest way to see how a SAR system works, and to derive the required signal processing operations to focus the image. In this way, we can observe the impulse response of the SAR, as the whole system is a linear system.

Slide 29

The range equation expressed the range from the antenna phase centre to the target scattering centre, as a function of pulse number or azimuth time. It is one of the most important equations in the SAR system, because the azimuth phase encoding, and the subsequent azimuth signal processing depend upon this change in range. It is the change in range which makes a SAR work, in the sense that it allows us to process the received data to get fine resolution in azimuth.

In both satellite and airborne SAR, it is common to use the straight line motion assumption illustrated in the sketch. The assumption is very accurate for airborne SARs; for satellite SARs it is also a good assumption with the proviso that Vr is allowed to change with range.


Slide 30

The received signal is demodulated because, in subsequent signal processing operations, we want to deal only with the information part of the signal, not the carrier.

However, the effect of the carrier frequency is very important, as the phase change 2πf0τd is a direct function of the radar carrier frequency or wavelength, λ = c / f0.

The demodulator multiplies the received signal by a coherent local oscillator. When the received signal is delayed, the phase of the local oscillator advances. In this way, the demodulation process changes the time delay τdinto the azimuth phase 2πf0τd .

Slide 31

This slides illustrates the flight geometry of a typical airborne SAR. The radar beam (not explicitly shown), begins illuminating the target while at point A, and finishes the illumination at point B.

During this interval, energy is received from the target. This energy is demodulated, sampled, and stored in SAR signal memory inside the signal processor. It could also be stored on tape or downlinked directly to the ground.

For each transmitted pulse, one line is stored in signal memory. As the range to the target R(η) changes, the energy shifts in signal memory, as illustrated on the next slide.

Slide 32

There are two significant azimuth times associated with this target, in addition to the exposure start and stop times. The first is the time when the centre of the beam crosses the target, and is denoted by ηc.

The second is the time that the target is closest to the radar, and is denoted by ηo. The latter time may not appear in the figure, if the beam squint is large enough that the target is not illuminated when it is closest to the radar system.

Slide 33

In order to illustrate the operation of the Range/Doppler algorithm, we have done a complete simulation using a single received point target.

We used parameters from the ERS satellite SAR, with the exception that we have shortened the range chirp length and the azimuth exposure time in order to fit the simulation into a 128 x 256 point array.

To achieve this shortening, we have increased the range and azimuth FM rates, to keep the bandwidths the same. Reducing the radar wavelength was one parameter changed to achieve this.

The simulation is still accurate, because the time-bandwidth products (TBP) are still over 100, a requirement for representative results.

Slide 34

This diagram shows the locus of energy in signal memory that would be received from a single point target on the ground.

This signal is important as it is used to define the SAR processing algorithms (the matched filters) and to define the impulse response of the end-to-end system, including the signal processor.


Note that the range migration is clearly seen. It appears step-like in this portrayal, because we have only plotted every 4th range cell (to keep the file size down).

Slide 35

Typical steps in the commonly-used Range/Doppler algorithm include:

• Unpack data from downlink format into complex (I,Q) words

• Balance the I & Q channels for gain and phase

• Range compression (fast convolution with weighting)

• Azimuth FFT (fast Fourier transform)

• Doppler centroid estimation

• Range cell migration correction (interpolation in range direction)

• Azimuth matched filter multiply (with weighting)

• Look extraction (select desired portion of Doppler spectrum)

• Azimuth IFFT (inverse fast Fourier transform)

• Detection*

• Look summation* * these operations are not done when complex images are desired

We will review the most important of these steps in the next group of slides. Note that Doppler Centroid Estimation is sometimes done before the azimuth FFT, depending upon the algorithm used.

Slide 36

In the next group of slides, we outline the main operations in range processing or compression.

Because the phase structure of the range signal is not significantly affected by range migration, range compression can be achieved by a 1-dimensional matched filtering operation along the range direction. If necessary, a secondary range compression can also be applied to improve range focussing.

The range compression operation is a conventional matched filtering operation, where the compression filter is applied in the frequency domain using FFTs. After the inverse FFT, only a portion of the output points is valid, because of the circular wraparound of the FFTs.

It is also useful to think of the matched filtering as a correlation between the received signal and a replica of the ideal received signal (with the latter conjugated, because the signals are complex). The matched filter will produce a strong, sharp output only when the phase structure of the received signal is well matched with the replica.

Slide 37

The first step is to find a replica of the transmitted range chirp. In some systems such as RADARSAT, a replica is embedded in the data stream of the received range lines. If not, the replica is generated knowing the duration, centre frequency and FM rate of the chirp.


To verify the correct matched filter, it is useful to look at the magnitude and phase spectrum of the replica and the matched filter.

In the left-hand plots, the magnitude spectrum is shown. In the top panel, the magnitude spectrum of the received datais shown. As this data contains only one point target with no noise, it can be used as the chirp replica. In the bottom panel, we show the magnitude of the spectrum of the matched filter, before weighting (in red) and after weighting (in green). Note that the shape of the spectrum of the matched filter before weighting is the same as the replica, and weighting tapers the matched filter energy at the edges of the spectrum.

The right-hand plots show the phase of the spectrum of the replica (top) and of the matched filter (bottom). They are designed to be equal and opposite to each other, as the main purpose of the matched filter is to match the phase of the signal.

Slide 38

This slide shows the result of compressing one range line containing a single point target. Before compression, the real part of the signal is shown, and after compression, the absolute value is shown.

The signal is a linear FM chirp centred at zero frequency after complex demodulation.

After compression, the width of the main lobe at the -3 dB level is shorter than the length of the uncompressed pulse by the ratio of the time-bandwidth product (TBP).

After compression, the point target looks like a sinc function. Compared to the usual sinc function, this pulse has a slightly wider main lobe, and lower side lobes, because of the smoothing action of the window.

Slide 39

A waterfall plot of the range compressed signal of a point target is shown in the left side of this slide (the absolute value of the complex number is shown). This time the whole azimuth exposure is shown, but for clarity, only every 15th line is shown.

The peaks have a wobbly appearance, as they are migrating through range cells, and no interpolator is used in this plot. However, an interpolator would show that the peaks are smooth.

On the right side, we show a mesh plot of the same data, but this time every 8th range line is shown. This subsampling in azimuth gives the peaks a rather spiky appearance, and the migration through range cells gives the side lobes a wavy appearance. However, the result is correct.

Slide 40

Finally we show a contour plot of range compressed energy. In this plot, the range migration is clearly seen, which will be corrected in a subsequent operation.

This time, every range line is contoured, but the migration through range cells still gives a wavy appearance to the plot.

Slide 41

The range resolution is a direct function of the processed range bandwidth, which is lowered a little by the weighting function.

The resolution can be expressed in a number of different units. The generic expression is given in seconds (or range cells), but it is also useful to express it in metres. This is done by multiplying by the effective propagation speed, which is one half the speed of light, or 150 m/µsec.


This gives the resolution in metres along the beam direction, referred to as the slant range resolution ρsr

To get the range resolution measured along the ground ρgr, the slant range resolution must be divided by the sine of the radar incident angle.

For ERS, ρsr= 9 m and ρgr= 23 to 30 m, depending upon the incident angle.

For RADARSAT, ρgr= 10 to 65 m, as it has a wide choice of range bandwidths and incident angles.

Slide 42

To examine the results in more detail, we use an interpolator to expand the sampling frequency in the range direction. Taking one range line, expanding by a factor of 16, and plotting the pulse magnitude on a dB scale, this plot is obtained.

Now we can measure detailed parameters of the compressed pulse, such as:

• -3 dB resolution

• the height of the maximum side lobe (MAXlobe)

• the 1-D integrated side lobe ratio (1-D ISLR)

• the phase at the peak of the pulse (Pkindex)

• the amplitude at the peak (Pkvalue) and

• the phase at the peak (Pkphase)

All parameters here have their ideal values in this example.

Slide 43

Next we plot the phase of the expanded pulse. Here we see that the phase is essentially zero everywhere. When the pulse amplitude is positive, as it is within the main lobe, the phase is almost exactly zero. When the amplitude changes sign, as it does for every second side lobe, the phase goes to either +180ο or - 180ο.

This excellent phase accuracy is due to the fact that the phase of the matched filter was carefully matched to the phase of the signal.

Slide 44

A required step before Range Cell Migration Correction (RCMC) is to get the data into the azimuth frequency domain, by taking an azimuth FFT.

This figure and the next one show the locus of target energy in the range-time, azimuth-frequency domain.

Because of the linearity of the frequency-time relationship of linear FM signals, the shape of the locus of target energy is the same as in the azimuth time domain, with the exception that the azimuth frequency axis is rotated with respect to the azimuth time axis to an arbitrary non-zero center frequency.

This centre frequency is directly proportional to the beam offset ηc and the azimuth FM rate Ka, and is given by:


Slide 45

This contour plot of azimuth frequency-domain energy illustrates the disjoint nature of the energy in the frequency domain, when compared with the azimuth time domain in slide 40.

However, it is not really disjoint --- the energy is simply circularly-rotated around the azimuth frequency axis. The rotation occurs because the actual azimuth frequency is many tens of KHz, but is aliased into the interval [ 0 : Fa ], where Fa is the azimuth sampling rate or PRF (pulse rate frequency).

Slide 47

In this slide, the Doppler energy is originally between M Fa and (M+1) Fa, where M is an integer. In this case, the complete Doppler centroid is at (M+1/2) Fa, and the observed Doppler centroid is at frequency Fa/2.

However, in general, the Doppler spectrum is not symmetrically placed between two integer multiples of Fa.

Slide 48

In this slide, the spectrum is not between integer Fa boundaries, but can lie anywhere along the azimuth frequency axis.

We want to estimate the complete, unaliased Doppler centroid, shown as Fcen.

From the observed spectrum, we can estimate Ffrac in a number of ways, which are relatively straightforward and reliable. But estimating the Doppler ambiguity number M is more difficult.

The earliest method of estimating Ffrac was to use a curve-fitting procedure on the blue curve. The earliest method of estimating M was to estimate the range shift in a multilook environment.

Recently, Doppler estimation methods based on signal phase were developed. One of these is illustrated on the next 2 slides.

Slide 49

In a method developed by Richard Bamler and Hartmut Runge of DLR (Deutsche Forschungsanstalt für Luft) in 1991, use is made of the fact that the Doppler centroid is directly proportional to the radar frequency (i.e. inversely proportional to the radar wavelength) to obtain both the fractional part of the Doppler centroid and the Doppler ambiguity.

As the radar pulse sweeps through its bandwidth (e.g. 17 MHz), the radar frequency changes by a small fraction (0.32 % in the ERS case). If we estimate the slope of Ffrac vs. range frequency, then the absolute Doppler centroid can be obtained. To do this, we perform the following steps on the range-compressed data in the range-time, azimuth-time domain:

• transform to the range frequency domain

• for each sample S(i) and the one following in the azimuth direction, compute conj(S(i)) * S(i+1)

• sum these terms over azimuth to obtain the average cross-correlation coefficient (ACCC)

• extract the phase angle of the sum (which is proportional of Ffrac)


• plot phase angle in radians vs. range frequency in Hz

• estimate the average value G1 and the slope G2 of this plot

• find the centroid by projecting the slope to the radar frequency

Steps 2 and 3 are illustrated in this slide. Each of the shorter lines radiating out from the centre represents the value of conj(S(i)) * S(i+1) at one azimuth time, all taken at the same range frequency. These complex vectors are then summed to obtain the longer vector with the circle on the end (shown scaled). The angle of this long vector is the ACCC angle at this range frequency.

Slide 50

These ACCC angles are then found for each range frequency, and are plotted in this slide. A straight line is then fitted to the central 75% of the range spectrum, and the average value G1 and the slope G2 is found.

We then compute the estimates of the fractional part, the ambiguity number and the absolute Doppler centroid using the formulae below. First, the fractional part is estimated by:

Then we project the slope G2 to the radar frequency to obtain the Doppler ambiguity number, M:

where Fintercept is the frequency where the plotted line intercepts the radar centre frequency. The projection of the slope is not very accurate, but M is obtained correctly if Fintercept is accurate to within +/- Fa / 2.

The estimated total Doppler centroid is then:

Slide 51

The total range cell migration depends mainly upon the synthetic aperture length, the range resolution, and upon the squint of the beam forward or aft of the zero Doppler. The synthetic aperture length and range resolution are fixed for a given radar system configuration (except for the linear increase of aperture with slant range), while the squint of the beam can vary with each data take.

The formula in the slide gives the range migration in range cells for the case where the squint angle is large enough that the zero Doppler point is not illuminated by the beam (if it is illuminated, the range migration is generally very small).

Vr = effective radar velocity (m/s)

Fr = range sampling rate (Hz)

c = speed of light (m/s)

R0= slant range (m)


If the RCM is greater than one range cell, then RCM correction (RCMC) should be performed.

In the graph, we draw the total RCM for our simulation parameters. These parameters use an exposure time somewhat less than the ERS satellite. In this case, ηc of 6.3 s corresponds to a squint angle of 3o. If ERS had the same squint angle, the RCM would be 34 range cells.

Slide 52

There are two steps in computing the required amount of RCMC for each azimuth frequency cell.

First, we must compute the absolute or unaliased frequency corresponding to each azimuth frequency cell. This is a linear relationship with a discontinuity of Fa. The discontinuity occurs at the azimuth frequency cell corresponding to frequency Ffrac + Fa / 2. The absolute frequency is then found by adding (M-1) Fa, M Fa or (M+1) Fa to the frequency of each cell, depending upon whether the DOPCEN is left or right of the discontinuity point.

Having obtained these frequencies, the range equation must be expressed as a function of azimuth frequency instead of azimuth time. This is done using the linear relationship

Then we obtain the RCM in cells vs. azimuth frequency. Strictly speaking, the RCM needed is a quadratic function of azimuth frequency. However, in C-band satellite SARs, the quadratic component is very small, so that the curve of RCM vs. frequency is almost linear. For this reason, we can annotate the right-hand axis in the figure with RCM, which closely portrays the correct RCM needed.

Slide 53

As the RCMC needed is usually some fraction of a range cell, we need an interpolator to move the data an arbitrary fraction of a cell.

Usually this fraction is quantized to 1/16 of a cell, so 15 different interpolators are needed to move the data by i /16 of a cell, where i = 1 : 15.

A simple interpolator is obtained from a truncated sinc function, as shown in blue. To avoid excessive frequency leakage in the interpolator, the coefficients are weighted by a Kaiser window with β = 3. After multiplying the coefficients by the window, the coefficients shown in red are obtained.

Slide 54

To get the 15 sets of coefficients, the red curve must be subsampled by 16, with the appropriate shift.

This slide shows 8 of the coefficient sets. Set 1 shifts by 1/16 of a cell, and set 8 shifts by 1/2 of a cell. Sets 9 to 15 are the mirror image of sets 7 to 1, while set 16 is the ``no-shift'' set = [ 0 0 0 1 0 0 0 0].

Slide 55

The RCMC operation is illustrated in this slide.

The amount of shift needed can be separated into an integer and a fractional number of range cells, as shown in panel (a). The integer cell shifts are performed simply by a shift of samples, while the fractional sample shift is performed by the interpolator.

Panel (b) shows the distribution of energy in every 16th range line prior to RCMC.


Panel (c) shows the distribution of energy after the integer shifts are performed. This shift corrects most of the RCM, but a significant amount of energy jitter remains.

Panel (d) shows the distribution of energy after the fractional shifts are performed with the interpolator.

We see that the energy is now well-aligned in azimuth, which is illustrated further in the next 3 slides.

Slide 56

This slide shows a mesh plot of signal energy, where every 12th line is shown.

Slide 57

To be sure that the energy does not appear elsewhere in the array, this slide gives the energy summed in the azimuth direction, including the energy from every range line.

Slide 58

This figure shows a contour plot of energy after RCMC.

Compare this plot with slide 44, which shows the contour plot of signal energy before RCMC. The alignment of energy along the azimuth direction is now complete, ready for azimuth compression.

Slide 60

The azimuth matched filter is generated and applied much the same as the range matched filter.

If multi-looking is done, only a fraction of the azimuth frequency data is used for each application of the matched filter.

Slide 61

To check the correct generation of the azimuth matched filter, the properties of the received data should be examined.

In this slide, we look at the magnitude (top) and phase (bottom) spectrum of the data in one range cell. As we have only a single point target in this simulation, we examine the range cell containing the majority of the target energy.

In the top plot, we note that the data has an appropriate oversampling ratio, i.e. the signal bandwidth is about 85% of the sampling frequency. We also note that the magnitude spectrum has a peak at about azimuth frequency cell number 33, which agrees with the DOPCEN frequency found by the estimators:

Note that in real data, the magnitude spectrum will be a noisy version of the top plot, but the phase spectrum will be random.

Slide 62

In this slide, we take a 30 x 30 point array centred on the largest value, and plot its magnitude with a mesh plot.

This gives an overview of the peak and its surrounding side lobes.


Slide 65

We see that the azimuth resolution is about 1.1 cells, a direct function of the weighting function and the oversampling ratio used. It is also due to the accurate definition of the azimuth matched filter, for if the azimuth FM rate were wrong, a coarser resolution would be obtained.

The first side lobe is down 18 dB, again a direct consequence of the weighting function used. The 1-dimensional integrated side lobe ratio (ISLR) is -16 dB, which is normal for the weighting function used.

The phase function is not quite perfect, with the answer being about 2 degrees off. This small error is a consequence of range migration, and the imperfect operation of the interpolator.

Note that the phase function has a distinct slope, because the Doppler centre frequency is not zero.



Radar Polarimetry


Radar Polarimetry

Polarimetry is the science of using measurements of the full polarization scattering matrix to infer physical properties of the Earth’s surfaceThe scattering matrix is measured by a SAR system by transmitting with two orthogonal polarizationsOn transmission, the two orthogonal polarizations are alternated on successive pulsesOn reception, the 2 polarizations are received simultaneously, leading to four channels of received datae.g. on odd pulses, HH and HV are measured, and on even pulses, VV and VH are measured.


Types of Linear Polarization




Choice of PolarizationBasic or operational SARs usually have only one polarization for economy, e.g. HH or VV

Research systems tend to have multiple polarizations, e.g. HH, HV, VV, VH (quad pol)

Multiple polarizations help to distinguish the physical structure of the scattering surfaces:– The alignment with respect to the radar (HH vs. VV)– The randomness of scattering (e.g. vegetation -

HV)– The corner structures (e.g. HH VV phase angle)– Bragg scattering (e.g. oceans - VV)


Benefits of Quadruple Polarization

The scattering matrix, Stokes matrix and polarization signature can be computed for each pixel

– can be a powerful classification tool

– for both visual and machine classification

The scattering matrix can be used to synthesize the return with any polarization– to investigate the scattering properties of different

surfaces

– to optimize polarization for optimum detectability


Current Polarimetric Radar Systems

Airborne radars– JPL AIRSAR P, L, C-bands– Canadian Convair-580 C, X-bands– Danish EMISAR L, C-bands

Spaceborne radars– NASA/DLR SIR-C/X-SAR


Future Polarimetric Radar Systems

RADARSAT-2– A fully polarimetric C-band satellite (2003)

ENVISAT– A C-band satellite with alternating polarization (2001)

SIVAM– An airborne SAR with full polarization at L-band– For Amazon River surveillance (2000)


Example of Multi-Polarization Imagery


Target Identification using PolarimetryL-Band HH SAR Image

Half Moon Bay, CaliforniaJuly 1994

Corner Reflector Cessna

Range

300 deg. east of TNFlight Path

Beechcraft

BeechcraftL-Band

BeechcraftP-Band

Cessna Polarization SignatureP-Band

Trihedralnatural world

Cylinderreturn weak in one direction

Dipoleno return in one direction

1/4 wavesecond direction delayed

Dihedral

Narrow Diplanedihedral with one direction attenuated

http://poes2.gsfc.nasa.gov/sar/becnless.htm http://www.radarresources.com/cj_spie97.pdf


HH VV Image Can Detect Aircraft in Foliage


Detection of Aircraft for Search and Rescue ~ Studies Using C-Band Polarimetric Data ~

Detection of parked aircraft at Carp Airport, March 18, 1999.

Detected and classified airplanesStorage boxBuildings

Studies conducted at CCRS using data acquired by the C-SAR on the Environment Canada CV-580, processed and calibrated at CCRS.

Source: T. I. Lukowski (2001) in CCRS Demo Products for RADARSAT-2 Applicationshttp://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/r2demo/demo6/oviewe.html

Trees

Buildings

Parking Lot Fuel Tank

Fence

Plane ParkingArea

Unpaved Road

Freight Containers


Processing of Polarimetric Data

Each polarization channel must be received and processed separately

The gain and relative phase of each channel must be carefully controlled and measured

The processing must be phase coherent

Data from each channel must be closely co-registered

Final 4-channel data is converted to Stokes matrix format and compressed


Polarization Requirements

– internal electronics– measurements from corner reflectors– measurements of uniform clutter

The polarimetric radar data must be calibrated for:

The calibration can be performed through a combination of:

– gain of each channel (channel imbalance)– phase of each channel (HH vs. VV phase)– cross-talk (e.g. H leaking into V channel)– noise correction– absolute radiometry


Convair-580 SAR Oxford County, Ontario

A: Corn stubble

B: Pasture

C: Stubble/tillage

D: Tillage field

A

B

C

D


Image Classification

Image classification is done from a set of parameters measured from radar imagesFor each frequency, 9 independent parameters are measured, as represented in the Stokes matrixPixels of the Stokes matrix can be averaged to reduce noise and improve classification accuracy, at the expense of spatial resolutionIn addition to the ampliture of each channel (or amplitude ratios), the co-polar phase difference (angle of HH VV*) is a powerful feature discriminator


Victoria & Saanich Peninsula SIR-C


Urban

Forest

Agriculture / Clear-cut

Suburban


Advanced Topics - Polarimetry

Notes

Slide 7

This image shows a colour composite, along with the 3 polarimetric components used to make the colour image.

Counter-clockwise from the top left corner:

Colour composite

HH red

VV green

HV blue

Slide 8

A polarization signature shows the magnitude and properties of the energy scattered from an object when illuminated by energy of a certain polarization. The illumination wave could be linear horizontal polarization, for example, but the scattered wave could have any orientation and ellipticity. The graph in the upper right panel shows the strength of the scattered energy as a function of orientation and ellipticity. This graph can be created from data received by a polarimetric radar.

Each scattering object has a unique polarization signature, which varies as a function of radar look angle and incident angle, as well as the radar’s frequency and polarization. Despite the complexity of the many parameters involved, the measured polarization signature can sometimes be used to identify specific objects at certain locations in the image.

Examples of reflectors which have unique scattering properties are given in the lower right panel. In the lower left panel, each group of pixels is marked with a symbol indicating the dominant scattering mechanism identified by the polarimetric radar. Note how the signatures of the Beechcraft aircraft differ between P-band and L-band.

Slide 9

The radar backscatter from an aircraft is dominated by double-bounce scattering, while foliage is dominated by what is referred to as “diffuse scattering”.

These types of backscatter are easily distinguished by a polarimetric radar, so if you have a radar which can penetrate the foliage (such as a P-band radar), you have a good chance of locating the crashed aircraft.

Slide 13

This is one of the earliest polarimetric radar images produced by the Canadian Convair-580 system. This C-band SAR composite image was created from HH and HV data, collected October 18, 1991.

(A) indicates a field of corn stubble in which no-tillage cultivation has been implemented.

Field (B) is a permanent pasture, considered a good conservation practice.

The corn stubble field (C) is a reduced tillage field.

Field (D) is a conventional tillage field and has a higher radar backscatter due to the surface roughness of the field.

Page 1 of 2Advanced Topics Notes - Polarimetry

Slide 15

Here is an example of how multiple polarizations and frequencies can be combined to provide useful terrain classification.

Page 2 of 2Advanced Topics Notes - Polarimetry


Radar Interferometry


Radar Interferometry

Overview of interferometrySatellite InterferometrySatellite InSAR geometryInSAR processing – measuring motion on the Earth’s surface– measuring topography

SAR examples


Radar Interferometry from Space

Drawing courtesy of Prof. Howard Zebker, Stanford University

Two satellites image the Earth’s surfaceOr one satellite takes two images a few days apartData are processed into complex SAR imagesThe phase difference of the two images is processed to obtain height and/or motion information of the Earth’s surface


Radargrammetry

An alternative to interferometry for estimating terrain heightTerrain elevation derived from the stereo portrayal of SAR amplitude imagesGenerally less sensitive than interferometryBetter than interferometry when:– topography is steep– coherence is low


Satellite InterferometryFor satellite interferometry of the repeat-pass type, one image is taken one day, and a second image is taken of the same scene one or more days later. – More images can be taken at later intervals and used in the

processing, as long as the scene retains reasonable coherence over the longer time interval

– Because there is always a time delay, and usually parallax as well, assumptions must be made or processing must be done to remove the unwanted component of motion or topography

In Feb. 2000, the Shuttle Radar Topography Mission obtained topographic (elevation) data of much of the Earth’s surface using single-pass interferometry, i.e., image pairs were acquired at the same time using two radar antennas separated physically to create a 60-m fixed baseline.


Satellite Repeat-pass InSAR Geometry2S

R 2

B

R 1

S1

Earth's surface h

A

S satellite positions

R range to point P

B baseline between satellites

A satellite altitude

h height of point P

B⊥

P


How a SAR Measures Distance

A radar is essentially a distance or range-measuring sensorIt can measure range in 2 ways:1. Time delay

• resolution = c / (2 BW ) = 8 m

2. Phase:• resolution = λ / 100 = 1 mm

Phase is much more accurate– but is a relative measurement only


O

Radar beam

Satellite

Surface

Phase

Transmitted Phase

Surface

Satellite

Radar beam

2Rλ

=

How a SAR Measures Phase


Phase after Scattering from a Random Surface

O

Radar beam

Satellite

SurfaceSurface

Radar beam

Satellite


Interferometer Phase

Radar beam

OO

SatellitesS

1

S2

ϕ

Phase φ

Surface

A

S1

Fringe separation

radians/fringe

Surface

Phase φ

ϕRadar beam

Satellites

A

S2

d Bdφ πϕ λ

⊥=4

λϕ⊥

=dB

0.5

λ

⊥

=R

B0.5 m


Radar beam

OO

SatellitesS

1

S2

ϕ

Phase φ

Surface

A

Mountain

rad/m

Surface

Phase φ

A

S1S2

Satellites

Radar beam

Mountain

ϕ m/fringe

4sin

d Bdh Rφ π

λ ϕ⊥=

0.5 sinλ ϕ

⊥

= RdhB

How Differential Phase Measures Topography


InSAR ProcessingProcess data to SLC imagesRegister the two images to 1/10 pixelOver-sample by a factor of 2 in both dimensionsFilter common bands in spectrumConjugate multiply to form interferogramSmooth the interferogramMeasure coherenceUnwrap phaseEstimate geometry parameters (especially baseline)Remove flat-earth fringesConvert unwrapped phase to height and/or motion


Measuring Coherence

Coherence must always be measured to assess the suitability of the data set for InSAR processingCoherence magnitude is closely related to the local standard deviation of differential phaseHigh coherence magnitude tells us:– images have good SNR– phase centres of scatterers are stable– any motion is spatially “organized”

Coherence formula:

( )( )*

2,1,

2 21, 2,

kkk

k kk k

I I

I I∑

∑ ∑


Use of CoherenceCoherence is mainly used as an interferometric quality checkCoherence magnitude: – 0.3 - 0.5 is useable, but noisy– 0.5 - 0.7 is good– 0.7 - 1.0 is excellent

Coherence has also been successfully used as a terrain classification parameter:– very low coherence → usually water– moderate coherence → often growing or moving

vegetation– high coherence → desert, city, or other stable

features


Satellite InSAR - Measuring Topography 1

To measure topography, the following conditions must exist:– the baseline must lie within acceptable limits– motion in the scene must be negligible– coherence must be high enough (e.g. | γ | > 0.4)

If the baseline is too small, the sensitivity to topography will be low, and phase noise may dominate – need B⊥ > 50 m for ERS

If the baseline is too large, phase aliasing may occur and the coherence will drop – need B⊥ < 300 m for ERS


Satellite InSAR - Measuring Topography 2

Limitations (aircraft SARs)– calibration of attitude– calibration of phase centres

Limitations (repeat-pass satellite SARs)– temporal decorrelation– low SNR

Accuracy– 1 – 3 m for aircraft– 5 – 20 m for repeat-pass satellites– depends upon coherence and topography


Flat Earth

Raw Interferogram

Gaussian Hill

Raw Interferogram

Gaussian Hill

F.E. Corrected Interferogram

Flat Earth

Raw Interferogram

Gaussian Hill

F.E. Corrected InterferogramRaw Interferogram

Gaussian Hill

Removal of Flat-Earth FringesInterferograms for flat terrain and a Gaussian-shaped hillAfter removal of the flat-earth fringes, the residual fringes form a “contour map” with:

m/fringe0.5 sinλ ϕ

⊥

=RdhB


Topography from InterferogramChitina River Valley, S.E. Alaska

0

200

100

Rel

ativ

e he

ight

(m

)

• B⊥ = 40 m• Flat-earth fringes

were removed.• Phase is still

wrapped.• Each revolution of

the colour wheel represents an increase of 200 m in altitude.

ERS images acquired Feb. 1994Courtesy of Dennis Fatland, Alaska SAR Facility


Topography Contours from InterferogramFranklin Bluffs and Sagavanirktok River on the North Slope of Alaska

Perspective view generated from an interferometrically derived DEM. The two ERS-1 images were acquired in September 1991.

Image shown courtesy of Rob Fatland, Alaska SAR Facility.


Vesuvius, the Volcano

SAR Image

Interferogram

DEM

Source:Ferretti,A., C. Prati,

F. Rocca and A.Monti Guarnieri, POLIMI, 1997


Perspective View from Height Measurements

Once terrain height is obtained, dramatic perspective views can be generated from the SAR dataIntensity = radar brightness, blue is “sky”Image courtesy of Prof. Howard Zebker, Stanford Univ.


Satellite InSAR - Measuring Motion 1To measure motion, the following must apply:

The time delay must be appropriate to the scale of motion to be measured (i.e. the motion must obey the Nyquist sampling theorem), The motion must have enough spatial cohesiveness that the coherence is high enough, Plus one of the 3 following conditions needed to remove the topographic component of phase:

– the baseline must be small enough that the topography component can be neglected,

– an accurate DEM must be used to remove the topography component, or

– three passes must be used to remove the topography component


Satellite InSAR - Measuring Motion 2

Motion has been successfully measured of:– glaciers (temperate and Arctic)– ice streams (Antarctica)– ice sheets (Greenland)– earthquakes– landslides– volcanoes

Accuracy– 2 cm/observation for C-band repeat-pass SARs


Satellite InSAR - Measuring Motion 3

While individual pixel motions may not be that accurate, satellite InSAR has the advantage over in-situ measurements by taking a large number of measurements over a wide areaIn this way, a velocity field can be constructed, and matched to a geophysical model of the motion (e.g.glaciers and post-seismic deformation)Examples on the following slides


Measuring Earthquake MotionScientists at JPL and CNES were the first to demonstrate that accurate motions on the Earth’s surface can be measured by satellite interferometryLanders is a desert area in California, so coherence is possible over a long time spanThe sensor is ERS-1, with a time lapse of 3 monthsDeformation lines (as shown) can be inferred by geophysicists using theoretical models

This image courtesy of Prof. Howard Zebker, Stanford University

RADAR IMAGE OF THE1992 LANDERS, CA EARTHQUAKE(EACH BAND REPRESENTS 2.8 CM

OF GROUND DEFORMATION)


Measuring Volcano Deformation

Motion interferogram of Fernandina Island, Galapagos

Fringes show intrusion of magma in a crack in the underlying rockMay be useful in

predictive studies of activity

Image courtesy of Prof. HowardZebker, Stanford University


Measuring Glacier MotionSaskatchewan Glacier, Canada

Perspective view of airborne SAR image based on DEMCoherence map (not shown)

DEM produced from Convair-580 cross-track interferometric dataRaw interferogram of glacier from ERS images of Nov. 2 & 3, 1996

– Interferogram intensity– Interferogram phase

Interferogram of glacier - pre- and post-correction for topographyHeight profile along glacier centreline Plot of glacier flow velocities

Petermann Glacier, GreenlandImage showing velocity measurements of glacier and ice stream


Perspective View & DEM of Saskatchewan Glacier

References: Cumming, I., J.-L. Valero, P. Vachon, K. Mattar, D. Geudtner and L. Gray, 1996Mattar, K.E., P.W. Vachon, D. Geudtner, A.L. Gray, I.G. Cumming andt M. Brugman, 1998


Interferogram Intensity and Phase

Reference: Cumming, I., J.-L. Valero, P. Vachon, K. Mattar, D. Geudtner and L. Gray, 1996


Interferogram Corrected for TopographyBefore correction After correction

Reference: Cumming, I., J.-L. Valero, P. Vachon, K. Mattar, D. Geudtner and L. Gray, 1996


Centreline distance (m) →

Hei

ght (

m)

→

Height of Glacier Surface from Convair-580 interferometer data

The glacier’s flow direction can be inferred from parameters extracted from the DEM, i.e., surface height, surface slope, and

slope direction along its centreline.


Glacier Motion Measurements

Phase due to topography is subtracted from the phase due to motion in the interferogram. The resulting motion fringes are processed to obtain line-of-sight glacier flow. The LOS displacements are projected to the glacier’s flow direction (inferred from the DEM) to obtain measurements of displacement of the glacier surface in the period between the ERS image acquisitions (Nov. 2 and Nov. 3, 1995)

Mattar, K.E., P.W. Vachon, D. Geudtner, A.L. Gray, I.G. Cumming and M. Brugman, 1998

Glacier Centreline (m) →

Surf

ace

disp

lace

men

t (cm

/day

)→

Saskatchewan Glacier Flow Rate

InSAR (Nov 2/3)InSAR (Nov 21/22)InSAR (Mar 5/6)InSAR (Mar 21/22)InSAR (Apr 25/26)NHRI (Aug-Sep 1995)NHRI (Sep-Dec 1995)NHRI (Dec-Feb 1995/6)Meier (1952-1954)


Glacier / Ice Stream Velocity Measurement

Image courtesy of Prof. Howard Zebker, Stanford University

An outlet glacier in North-Eastern Greenland.

Only the moving parts of the scene have been coloured. The black areas are areas where the coherence was too low to process.


The Convair-580 InSAR System

InSARAntenna Radome

Main Antenna Radome

Real-time Display Station

RF Equipment Racks

SAR Control Station

DigitalRecording

Convair 580


DEM of Kananaskis from the Convair-580 SAR

Source: Laurence Gray and Karim Mattar, CCRS


ConclusionsWe see that aircraft and satellite SAR can make accurate interferometric images under the right conditions The main limitation is scene coherence– SRTM solved this limitation for topography

Topographic accuracies can be:– 5 - 20 m for repeat-pass satellites– 16 m expected for SRTM– 1 - 3 m for aircraft

Velocity accuracies can be:– 2 cm/s for repeat-pass C-band satellites

Advanced Topics - Interferometry

Notes

Slide 1

In this group of slides, we will discuss how radar can be used to measure motion or elevations on the Earth’s surface using the technique of interferometry.

To measure motion or to measure topography, different system configurations and different signal processing algorithms are needed.

Radar interferometry can be operated on aircraft, space shuttles or satellites. We will give examples of each type of system, and mention their main distinctive points.

Slide 3

Radargrammetry was introduced by Franz Leberl and his group in Graz, Austria in the 1980s. It uses two SAR amplitude images taken with a cross-track parallax (a much larger parallax than used in interferometry). It is analogous to stereo photogrammetry popular in mapmaking today.

Radargrammetry depends upon identifying how much a feature has been displaced in one image compared to the second image. Its main limitation comes from the speckle in SAR images. When two images are taken with a parallax of more than a small fraction of a degree, the speckle pattern completely changes. This hides much of the fine detail of the image, to the extent that it becomes difficult to correlate features between one image and the other.

Radargrammetry uses correlation estimates to measure the displacement of the second image to an accuracy of about 0.5 pixel. The low sensitivity of radargrammetry comes from the fact that it takes a relatively large change in topography to move a feature by one pixel, while the same change in topography will cause many radians of phase change in an interferogram.

Interferometry has difficulties when the coherence is poor. This means that the phase of the interferogram becomes random and useless, but under the same circumstances, the amplitude of the two images may still be well correlated, allowing radargrammetry to obtain a parallax estimate.

When the topography is steep, phase aliasing may occur in the interferogram, making the fringes difficult to unwrap and interpret. Under the same conditions, the radargrammetry processor may still be able to correlate the terrain features. Severe layover and radar shadow will adversely affect both radargrammetry and interferometry.

Slide 5

When operated from free-flying satellites, two passes are used to obtain the two images. In this figure, the satellites are flying into the plane of the slide, with S1 representing the position of the first satellite pass and S2 representing the second satellite pass. The locations of the two passes are separated by the baseline B, which has a length (typically 100 m) and an orientation with respect to the horizontal.

Consider imaging the point P on a hill top having a height h above the Earth’s surface (the surface is represented by a geoid or nominal sea level). The range from the antenna of satellite passes 1 and 2 to point P is denoted by R1 and R2 respectively.

By measuring R2 – R1 and B very accurately (to 1 mm) using the phase differences of the two SAR images, and knowing R and A (to a few metres), the height h can be estimated to an accuracy of about 5 - 15 m.

Page 1 of 7Advanced Topics Notes - Interferometry

Slide 6

The traditional way of measuring range with a radar system is to note the time of arrival of the received signal with respect to the time that the pulse was transmitted. The accuracy with which time delay can be measured is equal to the inverse of the bandwidth of the system. When converted to range units, this accuracy is equivalent to c / (2 BW), where c is the speed of light.

The factor of 2 comes in because the radar signal has to travel a distance of twice the range to the target (i.e. it has to travel to the target and back again to the antenna). If the bandwidth is 20 MHz, then the range measurement is accurate to about 8 m.

If, however, the radar system is coherent and can measure the phase of the received signal relative to the phase of the transmitted pulse, a much more accurate distance measurement can be made. Usually phase can be measured to about 10o , so that the range can be measured to an accuracy of three hundredths of a wavelength.

This is in the order of a millimeter for C-band SAR systems, or 10,000 times more accurate than the “time of arrival” measurement.

However, there are millions of wavelengths between the radar system and the reflector, and we cannot count the total number, nor tell the phase fringes apart. Thus the phase measurement is only a relative measurement, and can be used only to tell the change in range from one measurement to the next.

Slide 7

The most important parameter that a SAR measures is phase, which equals twice the range R to the scattering centre of each pixel, divided by the radar wavelength λ.

In this slide, the phase of the transmitted radar signal is shown by the black and white fringes. You can see how the phase is proportional to range

. At some range, the beam hits a scatterer, is reflected back to the SAR antenna and is processed to a pixel in the SAR image. It is this range to the reflection point that governs the phase of the target at that pixel.

Note that as the signal is being transmitted, the phase is actually φ = R / λ. But by the time it has arrived at the receiver, the signal has traveled a distance 2R, so we always refer to the phase relation as

In this sketch, the distance between the fringes is greatly exaggerated, as the distance between the fringes is λ/2 and the real radar wavelength is only a few cm.

However, with a single SAR system, that phase is essentially random, because the range to the pixel’s scattering centre is random at the scale of the radar wavelength.

Slide 8

This slide shows the signal phase after it has been reflected by a rough surface.

However, the height of the surface is random on the scale of the radar wavelength, as the pixel size (e.g. 10 m) is hundreds of times larger than the radar wavelength.

Therefore the reflected or received phase is completely random, so that no useful information is in the received phase of a


single radar system.

However, when two measurements are made, the differential phase is not random, assuming that the rough surface scatters the same in both cases.

Slide 9

Now consider a second SAR, operating within about 100 m from the first SAR. With an aircraft SAR, the separation is typically 1 – 2 m. With repeat-pass C-band satellite SARs, the separation is in the range of 50 – 300 m. The satellite velocity vector is coming out of the slide.

If we make single-look complex (SLC) images from each SAR, and carefully register them, it is interesting to examine the difference in the phase of each pixel.

Because the scattering centres of a given pixel are almost the same for each satellite pass, the phase difference is no longer random, but is a precise measure of the difference in ranges of the two satellites to the scattering centre of each pixel.

If the two satellites have a component of displacement perpendicular to the radar beam (referred to as the perpendicular baseline B

⊥), the differential phase is a direct function of the beam nadir angle. The interferometric phase change with

respect to the beam nadir angle is:

and as B⊥is a slowly-varying function of ϕ, φ is almost a linear function of ϕ.

This property of the differential phase is illustrated in this slide. Because the fringes are a direct function of beam nadir angle, it is also useful to think of the change of nadir angle per fringe as

or that the fringe separation measured along a constant slant range arc as

Slide 10

What the interferogram portrays is the phase difference of each pixel at the range where the beam intersects the Earth’s surface.

When the Earth’s surface is flat, as in the previous slide, the fringes are almost evenly-spaced, with a gradual increase in spacing as the range increases. These are called the “flat-earth” fringes, and are well known as long as A and R are known to a few metres, and B to a few millimetres. These flat-earth fringes can then be removed from the interferogram.

Now consider the case shown in this slide where some topography is present in the scene. Here we show a hypothetical mountain, with equal slopes on the near and far sides. We see that on the side of the mountain nearest the radar, the fringes are compressed compared to the flat-earth fringes. The fringes are compressed the most when the slope of the


mountain is perpendicular to the radar beam, but this is not a good imaging geometry, as then the range resolution goes to infinity (at the onset of layover).

On the side of the mountain furthest from the radar, the fringes are wider apart. Here the fringe sensitivity goes down, but the radar resolution gets finer. When the slope is parallel to the radar beam, the fringe sensitivity goes to zero, which is also not a good imaging geometry (at the onset of radar shadow).

Running along the arc of a constant range line, the fringe and height sensitivity are given by the two equations in the slide.

So if the fringes are clear, and are sampled fast enough (at the Nyquist rate), the terrain height can be obtained from the calibrated interferogram.

Slide 11

One reference containing more detail on these steps is: http://www-ee.stanford.edu/~zebker

Slide 12

In the coherence formula, the sum is taken over a suitable region in the SAR image, where k is the pixel number.

If the area is made too small, the coherence estimates are too noisy. If the area is made too large, the coherence will be biased low because of true changes in differential phase. In ERS data, the area covered by the sum is often 2 range cells by 10 azimuth cells.

Ii,kis the complex amplitude of the kth pixel in image i, i = 1, 2.

Coherence is a complex number. The angle of the coherence value is the maximum likelihood estimate of interferometric phase, averaged over the area covered by the sum.

The magnitude of the coherence is a measure of the standard deviation of the interferometric phase estimate. Coherence magnitude = 1 means perfect phase estimates, mag = 0 means the phase estimates are pure noise. In practice, any value below 0.3 means that the phase estimates are too noisy to use.

Slide 16

This slide illustrates part of the processing done to extract topographic height from a pair of registered SLC images.

The left panel shows the interferogram phase assuming:

• the Earth's surface is flat

• there is no motion of the surface

• there is no noise or other decorrelating influences in the scene

Under these circumstances, the phase fringes are constant in azimuth (vertical direction in the plot) and have a linear trend in the range direction (horizontal). These are the familiar flat-earth fringes present in all raw interferograms.

Then if a Gaussian-shaped hill is present in the centre of the scene, the raw interferogram takes on the distorted appearance shown in the middle panel. This does not make much sense, but once the flat-earth fringes are subtracted from the phase, the scene topography is clearly seen, much like a contour map (see right panel in the slide).


Slide 17

Interferogram of the Chitina River Valley just north of the Bagley Ice Field in South-east Alaska. Here the colors represent topographic contours.

The perpendicular baseline separating the two satellites is 40 m, and the “flat-earth” fringes have been removed.

The phase has not been unwrapped , but the repeating colour wheel effectively portrays topographic contours. Each revolution of the colour wheel represents a 200 m increase in terrain altitude.

The radar data was acquired from the ERS satellite at the Alaska SAR Facility in February of 1994. The image is shown courtesy of Dennis Fatland of the Alaska SAR Facility.

Slide 18

Digital Elevation Models (DEM) can be generated only after calibration procedures that involve precise estimation of the baseline and least-squares fitting to ground control points. Even with this procedure, there can remain areas within the coverage of an image which have unknown height, as a result of terrain distortion effects produced by steep slopes (in mountainous areas).

A topographic perspective of the Franklin Bluffs and Sagavanirktok River on the North Slope of Alaska is shown on this slide. This perspective was generated from an interferometrically derived DEM which in turn was produced from two images of the area acquired by ERS-1 in September of 1991. Image shown courtesy of Rob Fatland, Alaska SAR Facility.

Slide 19

Claudio Prati and Fabio Rocca and their group at POLIMI have been some of the most innovative researchers in SAR interferometry from the beginning. Since every Italian’s favourite mountain is Vesuvius, it was natural that they applied their skills to making a DEM from ERS data.

In addition to these images, the paper cited below shows how they used multiple ERS images (with different baselines) to improve the accuracy of the DEM and to observe the atmospheric artifacts that sometimes plague satellite SAR interferometry.

The data is from 7 ERS Tandem Mission sets, from July 7, 1995 to April 13, 1996.

These images were copied with permission from: Multi-baseline SAR Interferometry for Automatic DEM Reconstruction A. Ferretti, C. Prati, F. Rocca and A. Monti Guarnieri Dipartimento di Elettronica e Informazione, Politecnico di Milano (POLIMI) Piazza Leonardo da Vinci 32, 20133 Milano, Italy.

3rd ERS SYMPOSIUM, Florence, 17 - 21 March 1997 http://earthnet.esrin.esa.it/pub/florence/papers/participants/program-details/participants/data/ferretti/index.html

Also see (for the interferogram): An Overview of SAR Interferometry Rocca F., Prati C., Ferretti A. http://earth1.esrin.esa.it/florence/program-details/speeches/rocca-et-al

Slide 21

Up to now, we have assumed that there is no motion in the scene, and we have been interested in measuring surface topography. Any motion of the surface between the data takes will upset the topography measurements.


On the other hand, the SARs high sensitivity to motion can be turned to our advantage. As long as the effect of topography can be removed, the SAR interferometer can be a sensitive instrument for measuring “organized” motion on the Earth’s surface.

Motion speed considerations:

• features which move very slowly such as glaciers or land subsidence need a time lapse of several days to get sensible readings

• only repeat-pass data collections appropriate

• features which are relatively fast moving, such as ocean currents require a sensor with a very short time lapse (e.g. 10 ms)

• only aircraft SARs appropriate

• ocean surface decorrelates after 50 - 100 ms

Slide 24

The Landers earthquake occurred on June 28, 1992.

The ERS-1 data were taken on April 24, July 3 and August 7, 1992, while the satellite was in a 35-day repeat orbit.

The pixel spacing is 30 m and the image size is 90 x 113 Km.

This data has been processed extensively by Dr. Didier Massonnet and his group at CNES in France, and by Howard Zebker, Paul Rosen, Richard Goldstein, Andrew Gabriel and Charles Werner of the Jet Propulsion Lab in California.

Slide 25

Deformation of volcano on Fernandina Island, Galapagos, due to intrusion of magma in a sill crack in the underlying rock.

The deformation signature is the finely spaced pattern of color fringes in the southwest corner of the island.

Measurement of the spatial distribution of the deformation gives constraints on magma motions at depth, and may be useful in predictive studies of activity.

Slide 32

This is an outlet glacier in North-Eastern Greenland.

Only the moving parts of the scene have been coloured. The black areas are areas where the coherence was too low to process.

Slide 33

This photo and drawing show the components of the Canadian Convair-580 airborne InSAR system. Note particularly the InSAR antenna mounted 2.4 m above the main antenna (in the upper photo, this second antenna is partially hidden behind the wing). This second antenna provides simultaneous reception with the main antenna, so cross-track interferometric data can be collected without any temporal decorrelation.


The upper photo was taken from the CCRS web page: http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/satsens/sarbro/sbc580e.html

The lower drawing was taken from the CCRS web page: http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/satsens/sarbro/planea.gif



LandApplications


Land ApplicationsOutline

• Advantages of SAR for land applications• Parameters which influence radar backscatter

• Sensor Parameters• Target Parameters• Other Site Factors

• Land use and land cover applications• Primary level mapping• Updating land cover maps• Monitoring of land cover and land use

• Recommended radar configurations


Land Applications of SARIntroduction

This first unit in the applications section presents:

• Background information for land applications of SAR

• General concepts for scattering mechanisms, sensor and target parameters, and their influences on SAR backscatter from land targets; factors specific to applications in forestry, agriculture, hydrology, geology, mapping can be found in these units

• Examples of land cover and land use applications


Advantages of SAR for Land Applications

• Sensitivity of SAR to target geometry • important for vegetation mapping• roughness characteristics can be important in distinguishing

targets • corner reflector effects can help in identifying target (i.e.,

mangroves, wetlands)

• Sensitivity of SAR to dielectric constant (water content)

• Capability of viewing under conditions that preclude observationby aircraft and optical satellites is important for many monitoring applications


Land ApplicationsParameters which influence radar backscatter

• Sensor Parameters• frequency • polarization • incident angle

• Target Parameters• surface roughness• soil characteristics• vegetation characteristics

• Other Site Factors• orientation effects• terrain relief• environmental effects


Sensor Parameters

Frequency• determines penetration depth into soil or vegetation• determines sensitivity of SAR to surface roughness• determines canopy components contributing to total backscatter

Polarization• vertically polarized waves interact with vertically structured vegetation• horizontally polarized waves have greater penetration to underlying soil• cross-polarizations are sensitive to the target volume and may be less

sensitive to row effects

Incident Angle• backscatter decreases as a function of incident angle • determines contribution of soil and canopy to total backscatter (larger

angles interact more with canopy; smaller angles have more soil interaction)• surfaces appear “rougher” at larger angles• largest incident angle effects are observed on smoother surfaces


Frequency Comparison: C-, L-, and P-BandsFlevoland, Netherlands Agricultural Scene

L-Band P-Band

C-Band

Multipolarizationcolour composites courtesy of JPL


Radar Backscatter as a Functionof Polarization

Source: Oh, Yisok, K. Sarabandi, and F.T. Ulaby, IEEE, 1992

Bare Soil Surface

Back

scat

ter in

g C

oef fi

cien

t σ°

( dB)

Incident Angle (degrees)


Melfort, SaskatchewanAgricultural Scene (July)

C - VV

C - HV

C - HH

AB

Polarization Comparison

AB

AB


INCIDENT ANGLE COMPARISONRADARSAT - 1

Standard Beam 1 (θ = 20° - 27°) Whitecourt, Alberta 96-Feb-12

Standard Beam 7 (θ = 45° - 49°) Whitecourt, Alberta 96-Jan-25

© 1996 Canadian Space AgencyImagery Courtesy RSI

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/images/alb/ralb01e.html


Target Parameters

• Surface Roughness Parameters• target scattering (scattering regimes,corner reflectors)• surface height profile and autocorrelation function• surface roughness criteria

• Soil Characteristics• surface roughness • water content• penetration depth• surface macro-structure

• Vegetation Characteristics• water content• vegetation structure and geometry


Target Scattering

DARK MEDIUM BRIGHT


Scattering Regimes


σ0

dB

SpecularSpecularScatteringScattering

DiffuseDiffuseScatteringScattering

ShadowingRough SurfaceRough Surface

Smooth SurfaceSmooth Surface

150 300 550 800

Surface roughnessSurface roughness is measured by the standard deviation of the surface height variation (or rms height) in wavelengths divided by horizontal correlation length in wavelengths.

VolumeVolume scatteringscattering results in a reduced (or missing) specular regime and a diffuse scattering regime that varies slowly with incident angle.


Dihedral Trihedral

Corner Reflectors


Target Parameters~ Surface Roughness Parameters ~

Source: Ulaby, F.T., R.K. Moore, and A.K. Fung 1986,

“Microwave Remote Sensing: Active and Passive”, Vol. II, p. 824

Hei

ght (

cm)

Displacement x ' (cm)

Corresponding Autocorrelation Function

Surface Profile

Distance x (cm)

ρ(x′

)H

iegh

tz(c

m)

The standard deviation of surface height (σ) and the surface correlation length ( l ) are vertical and horizontal measures of surface roughness.

The correlation length is calculated from the autocorrelation function:

' ( 1) ,1

x j xj

= − ∆≥

for

where is an integer

( )

1

11

2

1

'

N j

i j ii

N

ii

z zx

zρ

+ −

+ −=

=

=∑

∑

( ) ( )1

22 2

1

1

11

1

N

ii

N

ii

z N zN

where z zN

σ=

=

= − −

=

∑

∑

( ) 1l eeρ =

is the natural logarithm


Target Parameters~ Surface Roughness Criteria ~

A surface may be considered to beelectromagnetically “smooth” when

Rayleigh Criterion:σ < λ / 8 cos θ

Fraunhofer Criterion:σ < λ / 32 cos θ

(for targets where λ ∼ σ)

σ = rms height θ = incident angle λ = wavelength


Target Parameters~ Soil Characteristics ~

• Surface roughness (usually tillage related and measured using rms surface height and correlation length parameters)

• Water content of surface layer (soil moisture and complex dielectric constant)

• Penetration depth depends on soil moisture content of soils, radar frequency and incident angle

• Surface macro-structure (i.e. tillage row characteristics, tillage direction and seed bed structures)


Field 2 - Loam30.6% - Sand55.9% - Silt13.5% - Clay

T = 23ºC

Die

lect

ricC

onst

ant

εso

il

Volumetric Moisture mv

Relationship Between Dielectric Constant and Soil Moisture

Dielectric constant also depends on:• frequency• soil texture• soil temperature

Source: Ulaby, F.T., Moore,R.K., and Fung, A.K. 1986, “Microwave Remote Sensing: Active and Passive”, Vol. III, p. 2096


Radar Sensitivity to Soil Moistureas a Function of Incident Angle and Frequency

Frequency (GHz)

Sens

itivi

ty (d

B / 0

.01

g/cm

3 )

Source: Ulaby, F.T., R.K. Moore, and A.K. Fung, 1986, “Microwave Remote Sensing: Active and Passive”, Vol. III, p. 1872


Irrigation / Soil Moisture Influences

Irrigated Non-Irrigated

Potato Fields at Pre-Emergence Growth Stage

Outlook, Saskatchewan C-VV

Source:Pultz T. J. ,R. Leconte, R. J. Brown,B. Brisco, T. I. Lukowski, 1989


Soil Penetration Depthas a Function of Surface Soil Moisture Content

Source: Ulaby, F.T., Moore, R.K., andFung, A.K. 1986, “Microwave Remote Sensing: Active and Passive”, Vol. II, p. 852

Pene

trat

ion

dept

h (m

)

Volumetric moisture content mv (g cm -3)

Soil Type: Loam


Effect of Soil Macrostructureon Radar Backscatter

Source: Manual of Remote Sensing, Third Edition, 1998

Random Surface Component

Random Surface Component

Periodic (Reference) Surface

Mean (Reference) Surface

(a) Random height variations superimposed on a periodic surface, e.g. row-tilled surfaces

(b) Random height variations superimposed on a flat surface, e.g., mean surface

Source: Ulaby, F.T., R.K. Moore, and A.K. Fung 1986, “Microwave Remote Sensing: Active and Passive”, Vol. II, p. 822


Target Parameters~ Vegetation Characteristics ~

Characteristics which govern backscatter from vegetation:• Plant characteristics

• plant structure and geometry (shape, size, and orientation of leaves, stems, branches,…)

• water content in the plant (complex dielectric constant)• Characteristics of the vegetation canopy

• vertical and horizontal geometry (single or multi-layer, spacing between plants, row orientation)

• vegetation distribution and density (crown closure, proportion of ground cover) vis-à-vis influences from underlying ground surface (moisture, topography, soil)

• composition (mix of species)


Target Parameters~ Vegetation Characteristics ~

The backscatter from vegetation is used, with ancillary data, to infer information about• vegetation type• growth stage• vegetation condition or health• vegetation vigour or crop yield• planting, cultivation and harvesting practices• soil management practices • disturbances (e.g., fire, insects)• underlying soil characteristics and climatic

conditions• monitoring of compliance to laws and treaties


Scattering from Agricultural Targets

1

1 Direct Canopy (including multiple scattering)2 Soil / Canopy Interaction3 Direct Soil (including multiple scattering)

23

Source: Brisco, B. and R.J. Brown, 1998, “Agricultural Applications with Radar”, Chapter 7, Manual of Remote Sensing, 3rd edition, Vol. 2.


Differences in Backscatter due to Characteristics of the Plants

Melfort, Saskatchewan Airborne C-VV

Fallow

Wheat

Canola

July 1989 Resolution: 1.4 m (Rg) x 1.4 m (Az)


Sensors like RADARSAT-1 (C-HH) and ERS-2 (C-VV) provide one-dimensional data sets. Thus only broad crop classes (small grains versus broadleaf crops) can be detected with a single-date acquisition. However, once images from multiple dates are combined, most crop classes can beseparated.

In the multi-temporal composite presented here, the following crop types are detected:

1999 Canadian Space Agency

RADARSAT-1 ImageJune 02, 1999

Composite RADARSAT-1 ImageR:July 03 G: July 27 B: June 02

RADARSAT-1 ImageJuly 03, 1999


Seasonal Differences in Backscatter for Separation of Crop Types

Clinton, Ontario

green beansred wheatmagenta/pink barleyorange cornpurple alfalfa



Soil Backscattering

Soil - Trunk Reflection

(Corner Reflector)

Canopy - Soil Reflection

Scattering from Forest Targets~ Types of interaction ~


Effects on Forest Backscatter due to Changes in Water Levels

Lago Grande, Para State (Brazil)

Multi-temporal RADARSAT-1

S5D (Nov 28, 1996)S6D (Aug 7, 1996)

S6D (May 27, 1996)

Source: Costa, M.P.F., E.M.M. Nova, F. Mitsuo, J.E. Matovani, R.V.Ballester, F. Ahern 1998


Seasonal (Precipitation) Effects on Backscatter

Tropical Humid ForestWet vs Dry conditions

(Ivory Coast)

Wet SeasonAcquired on:

December 10, 1997

Dry SeasonAcquired on:

February 20, 1998

© 1

998,

Can

adia

n Sp

ace

Age

ncy

© 1

998,

Can

adia

n Sp

ace

Age

ncy


Other Site Factors

• orientation and row effects• In urban areas, brighter echoes are returned where the

house walls (corner reflectors) are parallel to the flight line.

• HH and VV backscatter, particularly at small incident angles, is significantly increased when the radar looks perpendicular to the direction of crop planting, harvesting and tillage rows.

• terrain relief• local incident angle effects

• environmental effects• rain, dew, wind, frozen soil


Courtesy of US Strategic Air Command

CARDINAL POINT EFFECT

Sun City



Geological Applications Laboratory

Vienna, AustriaRADARSAT-1 Jan - 25 - 1996

Beam F1 Sub-image θ:37°-40° C-HH Resolution: 6.0 m (Rg) x 8.9 m (Az)

Corner Reflector Effects


Other Site Factors ~ Row Direction Effects ~

LookDirection

B - Row direction is perpendicularto look direction- more backscatter

A - Row direction is parallel to look direction- less backscatter

Around-the-field cultivation pattern

A

B

A

B

B

A

Source: Hutton C. A., R.J. Brown, 1989


Row Direction EffectsSumaré, São Paulo State, Brazil

Fields planted with early tomatoes have large furrows (E) are very bright in this image. The tillage direction and the slope were perpendicular to the RADARSAT beam.

The topography and row direction of these cotton fields (F)produced high backscattering.

A = cornB = sugarcaneC = fallowlandD = pasturelandE = early tomatoes

with large furrowsF = cotton fields

Source: Epiphanio, J.C.N., M.S. Simões, A.R. Formaggio, C.C. Freitas, 1999http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/internat/glbsar2/imagery/bra/bra_22e.html

RADARSAT C-HH F5F January 05, 1998θ: 41 ° - 44° Resolution: 8.4m (Rg) x 8.4m (Az)

Display pixel spacing: 15.6 m

Descending pass

→

Look Orbit


Other Site Factors~ Terrain Relief ~

Local incident angle effects• Effect on backscatter mechanisms

• The topography modulates the backscatter response• Backscatter is enhanced when local incident angle gets

closer to 0 degrees• Other side effects (layover, shadow, foreshortening)

• Effects on image interpretability• LIA reduces discrimination between natural features• The brightness of the target is a function of the local incident

angle; the interdependent effects make interpretation difficult• Solutions

• Avoid acquisitions at small incident angles if possible


• Rain or dew on a target changes its backscatter characteristics and may reduce classification accuracies

• Wind may influence surface roughness, especially for water surfaces where the wave action increases the backscatter

• Frozen soils, regardless of moisture content, have a dielectric constant similar to dry soil

Other Site Factors ~ Environmental Effects ~


Agricultural Colonization in BrazilMulti-date Images and Environmental Effects

• April 23, 1996 - afternoon pass during wet season

• May 15, 1996 - morning pass during wet season; rain occurred during previous 24 hours

• Oct. 18, 1996 - afternoon pass on dry day at end of dry season (HIGHEST contrast)

• Oct. 23, 1996 - morning pass during dry season; rain occurred during previous 24 hours

• Oct. 30, 1996 - morning pass during dry season

Moisture reduces contrast between clearings and surrounding forest in C-band images Multidate RADARSAT - State of Acre, Brazil


S3 Desc. 96-10-23Source: Kux H.J.H. , J. R. dos Santos, F. Ahern, R. W. Pietsch, M. S. Lacruz, 1998

Kilometres

S7 Asc 96/04/23

S7 Asc 96/04/23

S7 Desc 96/05/15

S7 Desc 96/05/15 S7 Asc 96/10/18

S7 Asc 96/10/18

S7 Desc 96/10/30

S7 Desc 96/10/30

TM 543 96/06/14

S7 Asc 96/04/23

S7 Desc 96/05/15

S7 Asc 96/10/18

S7 Desc 96/10/30

TM 543 96/06/14

TM 543 96/06/14

Fazenda = Farm Rio = River —— road


AgricultureApplications


Agricultural Applications

• Scattering from agricultural targets • Agricultural applications

• Crop information (type, condition, damage)• Mapping soil management practices

• Important considerations during image acquisition• Incident angle • Timing of image acquisition• Environmental effects• Look direction and row direction

• Recommendations by sub-application


Scattering from Agricultural Targets

1

1 Direct Canopy (including multiple scattering)2 Soil / Canopy Interaction3 Direct Soil (including multiple scattering)

23

Brisco, B. and R.J. Brown, 1998, “Agricultural Applications with Radar”, Chapter 7, Manual of Remote Sensing, 3rd edition, Vol. 2.


Crop Scattering Contributions

WHEATXHH Band

σ°(dB)


Total backscatter

Ground-Crown-Ground

Crown-Ground

Ground-Crown

Direct Crown

Direct Ground


Crop Scattering Contributions


Incident Angle (degrees) Incident Angle (degrees)

WHEATσ°

(dB)

LHHLVV

CVVCHH

σ°(dB)

σ°(dB)

σ°(dB)

Total sigma0Ground_cover_groundCover_groundGround_coverDirect coverDirect ground


Source: Touré, A., K.P.B. Thomson, G. Edwards, R.J. Brown, and B. Brisco, 1994.


Crop Information

• Crop type and growth stage identification• Crop vigor evaluation• Crop damage identification and assessment• Yield estimation

For applications such as• Acreage and yield estimations for crop marketing

purposes (similar to Canadian Crop Information System)

• Crop insurance assessments• Marketing of products (seeds, fertilizer, herbicides,

implements)• Mitigation for fertility and infestation problems


Crop Type Identification

• Crop separability for a single date C-band SAR image provides three basic categories:1) smooth dry dark surfaces (e.g., summer fallow);

2) intermediate shades of grey as a function of soil moisture, surface roughness, and crop type interactions (forage and grain crops, e.g., wheat);

3) bright targets at or near saturation due to a high degree of both volume and surface scattering often including corner reflector like effects between plant/field geometry and incident microwaves (e.g., canola).


Crop Type InformationMelfort, Saskatchewan Airborne C-VV

Fallow

Wheat

Canola

July 1989 Resolution: 1.4 m (Rg) x 1.4 m (Az)


Sensitivity of C-Band Linear Polarizations to Different Crop Types

Of all three polarizations, the cross-polarization appears to be most sensitive to differences in crop type from field to field. However, each polarization provides some unique information and a three-band composite is required to separate all crop types.

Airborne CV-580 SAR data July 26, 1995

Altona, Manitoba (Canada)

Colour


Crop Condition• Radar is sensitive to crop structure and

moisture content• Changes in crop structure and moisture

content are indicators of crop condition and crop damage, for example:

• crop vigor related to biomass, leaf area index and height;

• water stress;• damage resulting from weather events (hail, wind,

rain); and• insect infestation.


Clinton, Ontario June 30, 1999

Correlation Between Backscatter and Crop Productivity

The SAR detects variability in the condition of this wheat crop.

Yield Map

Wheat Yield(bushels per acre)

CV-580 Airborne Radar (R=HH; G=HV; B=VV)


Wheat(variety AC Barrie)

(variety AC Elsa)

Barley (west field) and Oats (east field)Bright areas are crop residue

Wheat and BarleyRadar is detecting zones of moisture and nitrogen stress

CanolaPatterns are relatedto variations incrop biomass

CV-580 Airborne Radar Multipolarization composite (R=VV; G=VH; B=HH)

Areas of crop stress related to excess soil moisture earlier in the season are clearly evident in this multi-polarization composite.

Backscatter Response as a Function of Crop Condition

Indian Head, Saskatchewan June 28, 2000

N


Detecting Crop Damage with Radar

Altona, Manitoba - summer 1993

After eight months of heavy rain the American mid-west, in particular the Mississippi River system, was experiencing record flood levels. North of the American mid-west in the Red River Valley, the region aroundAltona also experienced above average rainfall. The resulting crop damage can clearly be observed in the SAR imagery where the "blow-down" or lodging of cereals and the uneven germination and crop growth create the mottled appearance of the fields. Healthier fields are a more uniform grey tone. Thus, the SAR data can be helpful in delineating the location and severity of crop damage.

C-HH image acquired by the JPL AIRSAR system

Altona


Biomass Effects

Source: Adapted from Ulaby, F.T., C.T. Allen, G. Eger and E. Kanemasu, 1984.

BA

CK

SCA

T TER

ING

C

OEF

FIC

IEN

T σ°

can

( m2

m-2

)

1979 Wheat 13.0 GHz VV 50 Deg

1980 Corn 13.0 GHz VV 50 Deg

LEAF AREA INDEXLEAF AREA INDEX

BA

CK

SCA

T TER

ING

C

OEF

FIC

IEN

T σ°

can

( m2

m-2

)

The backscattering coefficient of the canopy is dominated by leaf contribution if LAI is > 0.5 for corn. It is sensitive over a wider range of LAI for wheat (up to time of heading).

(σ° can = sum of contributions directly from the canopy and the soil as well as the multiple scattering between soil and canopy)


Tillage and Residue Management Practices

• Identification of type and amount of crop residue/trash

• Identification of type and timing of tillage and number of tillage applications

For applications such as• monitoring adoption of conservation practices• estimation of soil erosion as input into wind and

water erosion models (e.g., Universal Soil Loss Equation (USLE), Wind Erosion Equation (WEQ), Water Erosion Prediction Project model (WEPP))

• estimation of runoff into rivers and lakeshttp://www.agr.ca/pfra/pub/crsprair.htm


Effects of Different Tillage TreatmentsBa

cksc

atte

ring

Coe

ffici

ent(σo

)

Source: Brisco, B., R.J. Brown, B. Snider, G.J. Sofko, J.A. Loehler and A.G. Wacker, 1991.Incident Angle (θ)

0

-5

-10

-15

0

-5

-25

-30

-35

16 24 32 40 48 56 64

-10

-15

-20

-5

5

-10

-15

-20

-25

Ku

C

L

• Wheat stubble plots tilled with cultivator, cultivator plus rod-weeder, and disk harrow produce increasingly rougher surfaces, compared to no-till (control)

• All plots are confused at Ku-band where surfaces all appear rough. L-band shows little sensitivity as all plots appear smooth

• C-band provides most sensitivity as clod size approximates wavelength

Disk harrowhttp://www.casecorp.com/lar/english/agricultural/newequip/tillage/disk.html

Cultivator with rod-weeder attachment(rod turns the surface layer exposing

roots and leveling the soil)http://www.pima.ca/members/Hexirod.html

control (no-till) cultivator cultivator plus rod-weederdisk harrow


Important Considerations During Image Acquisition

• Incident Angle• Shallower angles provide better crop

discrimination (more interaction with the vegetation and less soil contribution)

• Shallower angles minimize contributions from soil moisture

• Shallower angles also maximize differences due to residue cover and tillage type


Crop Discrimination as a Function of Incident Angle

0

100

200

300

400

500

600

F Sum

10 15 20 25 30 35 40 50 60 70

Incidence AngleIncident Angle

Ground-based scatterometer measurements for 1987 growing season from western Canada

Source: Brisco B., Brown R. J., Gairns J., and Snider B., 1992.

F-sum = Arithmetic total of F - ratios between crop types calculated for each incident angle. The higher the F-sum the greater the information content for crop discrimination purposes.


Effects of Crop Residue on Backscatter

Source: McNairn H. , Boisvert J. B. , Duguay C., Huffman E. , Brown R. J. 1997

-14-12-10-8-6-4-20246

20 30 40 50

-14-12-10-8-6-4-20246

20 30 40 50

Corn Residue Grain Residue

Corn ResiduePlot 1 = low residue cover (harvester - removed plants,

leaving short stubble)Plot 2 = high residue cover (combine - removed only top

portion of plants to stip the cobs)Plot 3 = high residue cover (lying) (combine and mower

- removed cobs, then cut stalks leaving them lying on the field )Plot 4 = intermediate residue cover (harvester - removed

part of plants, stubble longer than in Plot 1)Plot 5 = control plot (bare)

Grain ResiduePlot 1 = intermediate residue cover (lying)Plot 2 = intermediate residue cover (standing)Plot 3 = high residue coverPlot 4 = low residue cover Plot 5 = control plot (bare)

C-HH BACKSCATTER (October 29) C-HH BACKSCATTER (September 25)

PLOT1 PLOT2 PLOT3 PLOT4 PLOT5

PLOT1 PLOT2 PLOT3 PLOT4 PLOT5

Incident angle (degrees) Incident angle (degrees)

Bac

ksca

tter (

dB)

Bac

ksca

tter (

dB)



• Timing - crops• Crop calendar is very important in crop classification• Multi-temporal data sets may be required, but must be corrected for

incident angle effects before quantitative extraction and modelling• Using the crop growth characteristics in relationship to the crop

calendar for the region of interest in a multi-temporal approach will provide additional information. Note the saturation effect and other such interaction mechanisms are a function of the system parameters so multi-parameter SAR (RADARSAT-2) is another approach for increasing information content.

• Timing - soil management practices• Conditions are very dynamic and timing is important• Multi-temporal data sets may be required to monitor farming practices

during post harvest and seed bed preparation


Growth Stage Dependence

JULIAN DATE (1980)

BA

CK

SCA

TTER

ING

CO

EFFI

CIE

NT σ°

can

(m2

m-2

)

Left Scale

Right ScaleFour-leaf

Five-leaf

Nine-leaf

Six-leaf

Half bloom

Soft dough

Hard dough

Harvested

Number indicates Vanderlip growth stageSIGMA0LAI

LAI (

m2 m

-2)

The temporal relationship of radar backscatter for a sorghum field with growth stages identified (after Vanderlip, 1972) and measured leaf area index (LAI). The radar frequency was 13.0 GHz with VV polarization at 50° incident angle.

Source: Adapted from Ulaby, F.T., C.T. Allen, G. Eger and E. Kanemasu, 1984.


Crop Discrimination as a Function of Crop Calendar

July 22 - Descending Orbitbarley beans canola corn flax oats sunflower

beans 1.82canola 1.69 0.05corn 1.77 0.01 0.02flax 0.04 1.67 1.50 1.61oats 0.01 1.92 1.83 1.89 0.08sunflower 2.00 1.05 1.61 1.35 2.00 2.00wheat 0.34 0.77 0.64 0.73 0.18 0.50 1.90

Average Divergence: 1.11

August 5 - Descending Orbitbarley beans canola corn flax oats sunflower


Average Divergence: 1.16

August 8 - Descending Orbitbarley beans canola corn flax oats sunflower


Average Divergence: 1.06Source: McNairn, H., R.J. Brown, J. Ellis and D. Wood, 1998.


Sensors like RADARSAT-1 (C-HH) and ERS-2 (C-VV) provide one-dimensional data sets. Thus only broad crop classes (small grains versus broadleaf crops) can be detected with a single-date acquisition. However, once images from multiple dates are combined, most crop classes can beseparated.

In the multi-temporal composite presented here, the following crop types are detected:


RADARSAT-1 ImageJune 02, 1999

Composite RADARSAT-1 ImageR:July 03 G: July 27 B: June 02



Crop Monitoring with Multi-temporal RADARSAT-1 Imagery

Clinton, Ontario

green beansred wheatmagenta/pink barleyorange cornpurple alfalfa


Growth Stages of Rice Paddy Crops

Stage 1

Stage 2

Stage 3


Backscatter from Paddy Rice

Backscatter from Land Cover Classes inZhao Qing, China

-25

-20

-15

-10

-5

015-Apr 25-Apr 05-May 15-May 25-May 04-Jun 14-Jun 24-Jun 04-Jul 14-Jul 24-Jul 03-Aug

Date

dB (B

eta

noug

ht)

Water

Rice

Aqua

Grass

Banana

• 12 dB change in rice areas from beginning of growing season to peak growing season

• Banana trees are consistently bright targets

• Grass provides constant returns of -5 to -8 dB, until flooded mid-season

• Water and aqua-culture are consistently dark between -19 and -24 dB

15-Apr 25-Apr 05-May 15-May 25-May 04-Jun 14-Jun 24-Jun 04-Jul 14-Jul 24-Jul 03-Aug

Date

dB (B

eta

noug

ht)

-- -- Water

— — Rice

— — Aqua

-- -- Grass

-- -- Bananas

Backscatter from Land Cover Classes inZhao Qing, China

Source: Ross S., Brisco B., Brown R. J., Yun S., Staples G., 1998


Rice Crop Monitoring Acquisition Schedule

RICE GROWTHSTAGE

RADARSATRESPONSE

INFORMATION

Acquisition 1 Early seasonflooded paddies

Specularreflections fromwater surfacesgives lowbackscatter

Field boundaries andpaddy locations

Acquisition 2 Mid-seasongrowing crop

Backscatterincreases due tomore surface andvolume scatteringas rice grows

Finalize rice growingregion, earlycondition estimates,acreage estimates

Acquisition 3 Late-seasonMature rice

Backscatterdecreases due tolower plant watercontent

Validation ofproduction region andfinal yield estimates

Source: Brisco B. , Brown R. J. , Stapes G. , and Nazarenko D. 1995


Zhao Qing, ChinaRADARSAT - Rice Crop Monitoring

RED6-Apr-96

F4 Descending

BLUE4-Aug-96

F4 Descending

GREEN17-Jun-96

F4 Descending

1996 Canadian Space Ageny Imagery Courtesy RSI



• Environmental Effects• Rain on the target increases backscatter and may

reduce crop classification accuracies• Dew on the canopy increases backscatter but may not

affect classification (relative differences between crops)• Rain and dew effects have implications for modelling and

quantification (when absolute differences are compared)• Residue classes are easier to distinguish when the

residue is wet, as occurs following a rain event

Source : Wood, D., H. McNairn, R.J. Brown et R. Dixon. 2001 "Using RADARSAT-1 for Crop Monitoring: Choosing Between Ascending and Descending Orbits". Submitted to Remote Sensing of the Environment.McNairn, H., C. Duguay, J. Boisvert, E. Huffman et B. Brisco. 2001. “Defining the Sensitivity of Multi-frequency and Multi-polarized Radar Backscatter to Post-Harvest Crop Residue”, Canadian Journal of Remote Sensing, in press.


Class Separability Due to Target Moisture Conditions

Source: Ulaby, F.T., R.K. Moore and A.K. Fung, 1986

Dry Conditions Wet Conditions

SCA

TTER

ING

CO

EFFI

CIE

NT

σ° H

V(d

B)

SCA

TTER

ING

CO

EFFI

CIE

NT

σ° H

V(d

B)

SCATTERING COEFFICIENT σ°HH (dB) SCATTERING COEFFICIENT σ°HH (dB)

C-Band (4.75 GHz)Flight 4Angle of Incidence θ : 50º

CornPastureFallow (Wheat Stubble

and Bare Soil)

C-Band (4.75 GHz)Flight 1

Angle of Incidence θ : 50ºCornPastureFallow (Wheat Stubble

and Bare Soil)Fallow

Pasture

Corn

Pasture

Fallow


Environmental Effects : Dew and Rain

June 27 (Asc)- June 28 (Desc) Dew Effect

July 21 (Asc)- July 22 (Desc) Dew Effect

August 14 (Asc)- August 15 (Desc) Rain Effect

August 21 (Asc)- August 21 (Desc) Dew Effect

-15

-13

-11

-9

-7

-5

-3canola wheat corn sunflower potatoes beans

Back

scat

ter

dB

Mean (ASC)

Mean (DSC)

-15

-13

-11

-9

-7

-5


Back

scat

ter

(dB)

Mean (ASC)

Mean (DSC)

-15

-13

-11

-9

-7

-5


Back

scat

ter

(dB)

Mean (ASC)

Mean (DSC)Mean (ASC)

Mean (DSC)

Mean (ASC)

Mean (DSC)

Mean (ASC)

Mean (DSC)

Bac

ksca

tter

dB

Bac

ksca

tter

dB

Bac

ksca

tter

dBcanola wheat corn sunflower potatoes beans

canola wheat corn sunflower potatoes beans


Source: Wood, D., R.J. Brown and H. McNairn, 1998

-15

-13

-11

-9

-7

-5

-3canola w heat corn sunflow er potatoes beans

Back

scat

ter

dBMean (ASC)

Mean (DSC)Mean (ASC)Mean (DSC)

Bac

ksca

tter

dB



• Look Direction and Row Direction• Row direction effects can be significant at or near

perpendicular look directions• For crops, row effects are prominent at incident

angles around 40o and at low vegetation densities• Sensitivity to these effects may be reduced using

cross-polarizations



Both C-HH and C-VV polarizations are sensitive to the direction in which this field washarvested. Where the row direction is perpendicular to the radar look direction, backscatter is significantlyhigher.

In the HV polarization, the look direction effect is virtually eliminated.

Colour

C- Band Airborne JPL AIRSARAltona, Manitoba (Canada) October 8, 1994

Look Direction

Polarization Comparison:Row Direction Effects


RADARSAT Recommendations by Sub-application

Application Preferred mode &incident angle (θ)

Advantages Disadvantages

Land Cover Mapping Standard (large θ) Sensitive to target roughness,structure and moisture

Geometric distortions dueto topography

Crop Information (type,condition, damage)

Standard or Fine (large θ) Sensitive to canopy structure Backscatter is alsodependent upon growthstage and crop condition

Soil Moisture Standard or Fine(small θ)

Sensitive to dielectric constant Roughness andtopography also influencebackscatter

Soil Tillage and CropResidue

Standard or Fine (large θ) Sensitive to surface roughness Soil moisture and rowdirection also influencebackscatter

Precision Farming Fine (large θ for cropinformation; small θ forsoil moisture information)

Sensitive to canopy structureand moisture, as well as soilmoisture

Resolution of currentsensors limits use tomapping zonal informationrather than site specific


ForestryApplications


Forestry Applications

• Forest Scattering• Environmental Effects• Boreal Forestland Applications

• Clearcuts• Fire Scars

• Tropical Forestland Applications• Cover Type Mapping• Deforestation Mapping• Forest Flood Mapping• Mapping of Fire Scars

• Recommended Radar Configurations


• Forest architecture or structure controls scattering behaviour for all • frequencies• polarizations• incident angles

• Response of σ° is a function of the relative importance of various scattering pathways, and sizes of scattering objects relative to the wavelength

• Dominant backscattering sources in forests:• Crown volume• Direct scattering from trunk• Direct scattering from soil surface• Trunk-ground scattering or ground-trunk scattering• Crown-ground scattering or ground-crown scattering

Forest Scattering Principles



Soil Backscattering

Soil - Trunk Reflection

(Corner Reflector)

Canopy - Soil Reflection

Forest Scattering~ Forest Targets ~


Forest Scattering~ Sources ~

Figure H.6 : C-Band Like-Polarized Canopy Backscatter Components vs Incident Angle

Canopy I, HH polarization

Bac

ksca

tterin

gC

ross

Sec

tion

(dB

)B

acks

catte

ring

Cro

ss S

ectio

n (d

B)

Canopy I, VV polarization

θ (degrees)

θ (degrees)

TotalGround - TrunkTotal CrownDirect Ground


Plots courtesy F.T. Ulaby


Forest Scattering~ Sources ~


Canopy I, VV polarization

Canopy I, HH polarization


θ (degrees)

θ (degrees)

Bac

ksca

tterin

gC

ross

Sec

tion

(dB

)B

acks

catte

ring

Cro

ss S

ectio

n (d

B)

Figure H.3 : L-Band Like-Polarized Canopy Backscatter Components vs Incident Angle

Plots courtesy F.T. Ulaby


Canopy -Water

Reflection

Water Backscattering


Water - Canopy Reflection(Corner Reflector)

Forest Scattering~ Flooded Forest ~


Forest Scattering~ Flooded Conditions ~

Modelled C-HH and C-VV backscatter from the

flooded igapó forest, Brazil

C-H

H B

acks

catte

r (d

B)

(b) Incident angle (deg.)

(a) Incident angle (deg.)

C-V

V B

acks

catte

r (d

B)

Modelled P-HH and P-VV backscatter from the flooded

igapó forest, Brazil

P-H

H b

acks

catte

r (d

B)

(a) Incident angle (deg.)


P-VV

bac

ksca

tter

(dB

)

Modelled L-HH and L-VV backscatter from the flooded

igapó forest, Brazil

L-H

H b

acks

catte

r (d

B)

L-VV

bac

ksca

tter

(dB

)(a) Incident angle (deg.)


c – canopy volume scattering,

d – trunk - ground term,

m – canopy - ground term, and

t – total backscatter (t=c+d+m)

Source: Wang, Yong and John M. Melack, IGARSS 1994


Source: Dobson, M.C. et al. 1992

Bac

ksca

tterin

g C

oeffi

cien

tσ°

(dB

)

Biomass (tons/ha)

C-Band

BIOMASS SENSITIVITY



Bac

ksca

tterin

g C

oeffi

cien

tσ°

(dB

)

Biomass (tons/ha)

L-Band

BIOMASS SENSITIVITY



Bac

ksca

tterin

g C

oeffi

cien

tσ°

(dB

)

Biomass (tons/ha)

P-Band

BIOMASS SENSITIVITY


Corn Field Forest

300 m

Spatially Uniform Target Fine Texture


Source: Ulaby, Moore and Fung, 1986

300 m

Image Texture


Environmental Effects~ Precipitation Effects ~

• Effect on image interpretability• In general, precipitation reduces the dynamic range

(i.e., contrast) within the scene

• Effect on backscatter mechanisms• Moisture becomes abnormally high, so backscatter

dominates the scattering process and the structure (architecture) of the target has a lesser role.


Environmental Effects~ Precipitation Effects ~

© 1

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August 4, 1996 (56.2 mm precipitation in previous 24 hours)

October 15, 1996 (dry conditions)

RADARSAT-1 Fine Mode Beam 4, Asc. Whitecourt, Alberta

ClearcutsForest

Forest

Forest

Forest

© 1

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Environmental Effects~ Precipitation/Seasonal Effects ~

Tropical Humid ForestWet vs Dry conditions

(Ivory Coast)

Wet SeasonAcquired on:

December 10, 1997

Dry SeasonAcquired on:

February 20, 1998

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Environmental Effects~ Local Incident Angle Effects ~

• Effect on backscatter mechanisms• The topography modulates the backscatter response• Backscatter is enhanced when local incident angle (LIA)

gets closer to 0º• Related effects: layover, shadow, foreshortening

• Effect on image interpretability• Discrimination between natural features is reduced • The target will be characterized by a signature which is a

function of the LIA, thus making interpretation difficult• Solutions

• Avoid steep incident angles


Environmental Effects~ Local Incident Angle Effects ~

Radar Shadow

θlocBrighter -smaller localincident angle

Nominal Brightness

Darker -larger local

incident angle

θloc

θloc


Environmental Effects~ Local Incident Angle Effects (LIA)~

Small LIA

Nominal LIA

Larger LIA

Look direction* All arrows are pointing at clearcuts / deforested areas

Ridge of the hill Slope facing the SAR

Slope facing away from the SAR


Boreal Forestland Applications

• Clearcut Mapping

• Fire Scars Mapping


• Effect on image interpretability• In general, the clearcuts have lower backscatter than

the natural forest• Effect on backscatter mechanisms

• Different structures of tree architecture, local topography (e.g., site preparation), and slash are observed

Boreal Forestland Applications~ Clearcut Mapping ~


Contrast between forestland and clearcutsWHITECOURT, ALBERTA 96-Jan-25

RADARSAT-1 Beam S7 (θ = 45° - 49°) C-HH Resolution: 20m (Rg) x 27m (Az)

Asce

ndin

g Pa

ss (R

ight

Looo

king

) —

——

——

——

—→

Full Swath - Pixel Spacing for Display: 56 m 1996 Canadian Space AgencyImage Courtesy RSI


Imag

ery

Can

adad

ian

Spac

e Ag

ency

, 199

6

April 5, 1996: Fine Mode Beam 5 Asc.

March 5, 1996: Fine Mode Beam 4 Desc.

RADARSAT-1 Whitecourt, AlbertaEffects of Look Direction, Local Incident Angle,

and Seasonality on Clearcut Discrimination

Imag

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, 199

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Forestland Information Group


Factors affecting contrast(decreasing order of importance)• Snow wetness• Slope and aspect relative to illumination• Surface roughness and slash• Residual vegetation

Clearcut Mapping � Recommended Configurations • Select optimal seasons to increase contrast between

forest and clearcuts• Most suitable season is when clearcuts are

covered by wet snow

Boreal Forestland Applications~ Clearcut Mapping ~


Boreal Forestland Applications~ Fire Scars Mapping ~

- What influences the interpretability -

• Fire type• Crown versus ground fire

• Target• Dielectric (water content)• Architecture / structure

- tree architecture- stand characteristics (composition, density)- ground characteristics (vegetation, roughness)

• Geometry• Sensor-Target (including topography)

• Sensor• Frequency, polarization (transmit and receive configurations)


Forest Fire Mapping Updatewith RADARSAT

Labeau Lake - Québec(50° 45′ N 75° 30′ W)

LANDSAT TM : 1996 RADARSAT-1 S1 Beam Desc. : May 5, 1998

Image Courtesy: Ministère des Ressources Naturelles du Québec 1998, Canadian Space Agency

Vectors Extracted from RADARSAT


Fire Scars Mapping Recommended Configurations

• Acquisition time after the burn• During or shortly after, the burned areas are not

always apparent unless major structural change of the canopy structure has occurred

• In the spring season (wet conditions), following the fire event, the burned forest can be mapped

Boreal Forestland Applications~ Fire Scars Mapping ~


Tropical Forestland Applications

• Cover Type Mapping

• Deforestation Mapping

• Forest Flood Mapping

• Fire Scars Mapping


Tropical Forestland Applications ~ Cover Type Mapping ~

Mapping Classes• Forestland cover types

– Primary/secondary forests – Plantations– Disturbed forest

• Pastures and cultures villages (agroforestry)• Wetland• Savannah...


Tropical Forestland Applications~ Cover Type Mapping ~

Tropical Environments • Heavily vegetated, always humid• Dense forest canopy acts as a surrogate for topography • No SAR backscatter from ground for high frequency SAR

Savannah Environments• Sparse vegetation, dry soil conditions• Backscatter mainly controlled by soil moisture and surface

roughness• If possible, avoid precipitation events



RADARSAT-1 Mosaic

Beam Wide 1, Ascending

June 9 & 16, 1998

Roraima State, Brazil

© 1997 Canadian Space Agency Image Courtesy RSI



Wetland mapping (Roraima, Brazil)

RADARSAT-1 Beam Ext. Low, Desc. : June 22, 1998

Wetland with standing vegetation

Wetland withoutstanding vegetation

Floodplain

Forest

Forest



Forest type mapping (Ivory Coast)

Multi-date RADARSATStandard 7

Dec. 10, 1997(R); Feb. 20, 1998 (G);

texture (B)

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RADARSAT S7Dec. 10, 1997(R); Feb. 20, 1998 (G); texture (B)

© 1

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LEGENDFO = Primary & Secondary ForestPF = PlantationsNI = Swamp ForestRA = RaphiaSC = Secondary & Mixed Agro ForestryCS = Mixed Agro & Secondary ForestCC = Mixed Agro FroestrySV = Savannah


Cover Type Mapping Recommended Configurations• Incident angle

• Shallow angles provide better discrimination for forestland cover mapping

• Shallow angles preserve information on deforested areas (riparian vegetation, regeneration, crop…)

• Steep angles provide better distinction between forest vs non-forest in flat areas; images acquired at these steep angles suffer fromgeometric distortion which compromises the spatial accuracy

• Timing• Dry season imagery shows better discrimination between forestland

classes, compared with wet season imagery• Wet season allows better discrimination of forestland classes, when

combined with a dry season acquisition



Cover Type Mapping Recommended Configurations• Optimal dataset

• Multi-date acquisitions (wet and dry season) combined with a texture/contrast channel offer the best results

• Imaging Mode• Fine mode should be used to detect roads, fine features, and

riparian vegetation• Multi-beam approach should be considered when mapping a

wide territory • Frequency

• Determines the penetration within the canopy, and samples different parts of the forest canopy



• Why map deforestation ?• Monitor deforestation

– planned– unplanned

• Monitor extraction from forest reserves• Assess agricultural expansion

Tropical Forestland Applications ~ Deforestation Mapping ~


• Deforestation for Cattle Ranching• current assessment of changes in deforested

area• riparian vegetation, regeneration in ranch

• Problems• deforestation

– biodiversity, potential for major climate change

• questionable sustainability– soil fertility, soil erosion, natural succession,

water quality



• Agricultural Colonization• location of unplanned colonization• changing boundary of cleared area• productivity• land use: pasture vs crop vs fallow

• Problems• Unplanned colonization• Failures of planned colonization

– sustainability issues -> repeated migration– accumulation of land by fewer and fewer owners– excessive deforestation -> loss in biodiversity,

release of carbon to atmosphere



Canada Centre for Remote Sensing (CCRS)Canadian International Development Agency (CIDA)Instituto Nacional de Pesquisas Espaciais (INPE)

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ProRADAR

Example of Tropical DeforestationHumaita Settlement, Acre, Brazil 98-May-15

RADARSAT-1 Standard Mode Beam 5 (θ = 36°-42°) Resolution: 24.2m (Rg) x 27m (Az)



Forest Change Vector: 1996 to 1999 (S6 Detection)

RADARSAT-1 S6 Beam, Desc. : 31 January 1999Landsat TM: Bands 5 4 3, 1996

Imag

e C

ourte

sy IN

PE

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mag

e C

ourte

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SI

State of Acre, BrazilChange Detection Vectors in Tropical Rainforest


Tropical Deforestation Recommended Configurations• Incident angle

• Shallower angles provide better discrimination between forest and deforested area

• Shallower angles preserve information on deforested area (riparian vegetation, regeneration, crop…)

• Steep angles (Extended Low or Standard 1) provide better distinction between forest vs non-forest in flat areas. Images acquired at these steep angles suffer of geometric distortion which compromise the spatial accuracy

• Beam mode• Fine modes should be used to detect roads, fine features, and riparian

vegetation• Multi-beam approach should be considered when mapping a wide

territory



Tropical Deforestation Recommended Configurations• Polarization

• Cross-polarisation (HV or VH) provides better discrimination of man-made features (depolarization) - (RADARSAT-2)

• Timing• Dry season imagery shows better discrimination between forestland

classes, compared with wet season imagery• Optimal dataset

• Multi-date approach provides better accuracy on state of growth in deforested areas

• Dry conditions are preferred (end of dry season is best)



• Forest flooding• Extent of flooding• Floodplain lakes• Floodplain vegetation

– aquatic– terrestrial

Tropical Forestland Applications ~ Flood Mapping ~



Tucurui Reservoir, Para State (Brazil)


S6A (Dec 5, 1996)S5A (Aug 14, 1996)S6A (May 27, 1996)



Lago Grande, Para State (Brazil)


S5D (Nov 28, 1996)S6D (Aug 7, 1996)

S6D (May 27, 1996)



RADARSAT-1March 23, 2000

RADARSAT-1March 1, 2000

RADARSAT-1Feb 28, 2000

© 1

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Flood extent, Limpopo River (Mozambique)


Flood mapping Recommended Configurations • Incident angle

• Medium angles (30o - 40o) are a compromise for discrimination of forest types and low density, flooded forest

• Frequency• High frequency can detect low aquatic vegetation (detects macrophytes

missed by L-Band)• Low frequency is better to detect water under closed canopies

• Timing• Multi-temporal imagery allows the monitoring of growth and movement of

floating vegetation

• Optimal dataset• A multi-frequency dataset would lead to better characterization of the

dynamic ecosystem of a periodically flooded area



• In tropical forest…

• burned forest scars are not always detectable (function of fire type and the state of structural change of the forest)

• reaction time is critical for data acquisition (tropical ecosystem is very dynamic)

• In tropical forest… crown fire results in

• increased backscatter from burned forest at steep incident angles

• decreased backscatter from burned forest at shallow incident angles, but it is harder to detect scars than at steep incident angles

• Detectability of fire scars is a function of the type of fire• if the upper strata (layer) of forest has not been affected

significantly, the likelihood of detecting the fire scars is considerably reduced

Tropical Forestland Applications ~ Mapping of Fire Scars ~



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Multi-Date RADARSAT (Red = S3, Green = S7, Blue = Difference)

Recent BurnsAcre State,

Brazil

RADARSAT S7 Asc. April 23, 1996 RADARSAT S3 Desc. October 23, 1996 (with 18.5 mm Rainfall)


Mapping of Fire Scars Recommended Configurations • Incident angle

• Steep angles allow better discrimination between burned and un-burned forest for crown fire type

– e.g., Standard 1, 2

• Timing• Response time is critical; acquisition should be done during or close to

the end of the dry season

• Optimal dataset• Multi-date acquisition increases chances to map fire scars (crown fires)• Acquisitions during the fire period (dry state) and early in the wet

season provide the optimal dataset

Tropical Forestland Applications ~ Mapping of Fire Scars ~


Forestland Applications

Summary and Recommendations


Forestland Applications~ Summary and Recommendations ~

Beam Selection Considerations • Steep incident angle

• Greater radiometric effects due to modulation of the relief (seelocal incident angle)

• Less return from architecture of the target• More sensitive to moisture (refers to scattering regime)

• Shallow incident angle• The structure of the tree contributes more to the backscatter,

so forest elements with different architectures will be discriminated easier

• Topography has a lesser role in backscatter mechanisms since the local incident angle will often be greater than 20 degrees


Acquisition Planning Considerations • Use seasonal changes to your advantage

• Dry season provides better contrast • Avoid rainy season and high dust months if you plan a single

acquisition

• Select time of acquisition (ascending vs descending)

• Daily changes occur in vegetation moisture

• Exploit vegetation phenology• Leaf-off vs leaf-on


NOTE: Always monitor the precipitation from 2 days before to the acquisition time.



Data Fusion Considerations• Dry season imagery normally has greater dynamic range (more

discrimination of land features possible) • Multi-temporal dataset adds new dimensions and better

discrimination for mapping purposes• Composite of images acquired during periods with dry and wet

conditions plus a texture channel (contrast) of the dry-period acquisition provides better discrimination

• RADARSAT-TM fusion using intensity-hue-saturation (IHS) transformation technique seems to provide better qualitative results - especially for visual interpretation

• SAR-Optical data fusion provides better optimization of the dimensionality offered by remote sensing data (radiometry and texture)


GeologyApplications


Geological Applications

Outline

• SAR and Geology– Terrain relief and SAR

– Look direction

– Environments (Tropical, Polar, Desert)

– Data Integration

– Stereo image pairs

• Applications– Geological mapping

– Mineral exploration

– Hazards mapping


Terrain Relief and SAR

• Low relief environment (~ 100 m)→ backscatter controlled by changes in local incident angle and surface roughness

• Surface roughness controlled by

– weathering process of the bedrock

– “reworking” processes of unconsolidated surficialdeposits (e.g., fluvial sorting, glacial action, wind erosion)


Low relief environment

Standard ModeBeam S2 Ascending


StrandlineLandslide

Ground moraineAlluvium

Deltaic deposits4 km

17-Oct-96Incident Angle: 24º - 31º

Resolution: 22 m (Rg) x 27 m (Az)Displayed Pixel Spacing: 27.3 m



Comparison of RADARSAT Viewing Geometryof Low Relief Terrains at Morden, Manitoba

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Singhroy V. , R. Saint-Jean, 1999. Effects of relief on the selection of RADARSAT-1 incidence angle for geological applications; Canadian Journal of Remote Sensing , Vol. 25, No. 3, 1999, pp. 211-217

http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/papere.cfm?BiblioID=4723


Intermediate relief

environment

Effect of SAR Incident Angleon Terrain Mapping

RADARSAT-1 Whitecourt, Alberta

RADARSAT-1 C-HH 96-Feb-12Ascending OrbitRight LookSTANDARD ModeBeam 1Inc. Angle: 20º - 27ºResol.: 26 m (Rg) x 27 m (Az)Partial SwathDisplayed Pixel size: 56 m

RADARSAT-1 C-HH96-Jan-25

Ascending Orbi tRight LookSTANDARD ModeBeam 7Inc. Angle: 45º - 49ºResol.: 20 m (Rg) x 27 m (Az)Partial SwathDisplayed Pixel size: 56 m


Canadian Space Agency, 1996

OrbitLook

OrbitLook

Singhroy V. , R. Saint-Jean, 1999. Effects of relief on the selection of RADARSAT-1 incidence angle for geological applications; Canadian Journal of Remote Sensing , Vol. 25, No. 3, 1999 , pp. 211-217



Terrain Relief and SAR (cont...)

• High relief environment (~1000 m)→ backscatter strongly controlled by angle and orientation of slopes

• Yields a very refined “terrain-texture” image of the landforms

• Erosional processes which define the landforms are often diagnostic of the underlying rock type

• Interpretation of high relief SAR imagery must contend with the effects of radar foreshortening, layover and shadow


High relief environment

Comparison of RADARSAT Viewing Geometryof High Relief Terrains, Hope, B.C.

Extended High ModeBeam EH6 Ascending

17-Oct-96Incident Angle: 57º -59º


Faults




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Legend

Block slideTransverse ridgesSlide scarpTransverse block fracture

Look

Singhroy V. , R. Saint-Jean, 1999. Effects of relief on the selection of RADARSAT-1 incidence angle for geological applications; Canadian Journal of Remote Sensing , Vol. 25, No. 3, 1999 , pp. 211-217



Effect of incident angle

RADARSAT-1EH6θ : 57° - 59°

RADARSAT-1Standard 5θ : 36° - 42°

RADARSAT-1EL1θ : 10° - 23°

Canadian Space Agency, 1996Received by the Canada Centre for Remote SensingProcessed and distributed by RADARSAT International Inc.

Sarawak, MalaysiaIncident Angle Effect on Terrain Appearance

Orbit

Look

Orbit

Look

Orbit

Look

D'Iorio M. , P. Budkewitsch, N.N. Mahmood, 1997.



Look direction

• Since SAR sensors provide their own illumination source, the look direction can influence the information content of the imagery.

• Greater morphological enhancement can be obtained when illumination is perpendicular to the topographical features (cardinal effect).

• In low relief environments, the look direction can be used to provide a greater enhancement of lineaments.

• In high relief environments, the look direction can be used to provide information on areas that are occulted the the other look direction or subject to layover or foreshortening.


Effect of look

direction

Sarawak, Malaysia

Ascending pass(east-looking)

RADARSAT-1date: 26 August 96beam mode: Standard (S6)incident angle : 44º

Descending pass(west-looking)

RADARSAT-1date: 3 June 96beam mode: Standard (S6)incident angle : 44º

layering apparent layering not apparent

layering not apparent layering apparent

Geological Application: effect of look directionTropical forest environment : interlayered sandstone and shale

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D'Iorio M. , P. Budkewitsch, N.N. Mahmood, 1997.



Tropical Environments

• Heavily vegetated

• Always humid

• Tropical weathering of bedrock reveals structures and rock type

• Dense forest canopy acts as a surrogate for topography → no SAR backscatter from ground


Geomorphology in tropical

environments

MATO GROSSO, BRAZILRADARSAT-1 23-Dec-98

Extended High Mode (EH6)Incident Angle: 57º - 59º

Resolution: 18 m (Rg) x 27 m (Az), Pixel Spacing: 40 m

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Descending Pass


Polar Environments

• Sparse vegetation

• Frozen ground

• Bedrock weathered by frost action ; related to rock type

• Thin, dry snow cover is transparent to SAR

• Best imaged during frozen ground conditions→ eliminates soil moisture effects


Lithology in polar

environments

BATHURST ISLANDLithological discrimination (roughness)

at low and moderate incident angles

Geological MapKerr, 1974(1:250,000 scale)

Standard 721 March 96incident angle: 45º - 49º

resolution: 20 m (Rg) x 27 m (Az)display pixel spacing: 60 mº

Extended Low 117 February 97incident angle: 10º - 23

resolution: 36 m (Rg) x 27 m (Az)display pixel spacing: 60 mº

Canadian Space Agency, 1996-97

Paul Budkewitsch, Marc A. D’Iorio, and J. Chris Harisson. 1996.

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/images/nwt/rnwt01e.html


Lithology in polar

environments

BATHURST ISLANDPOLAR BEAR PASS

Lithology from SAR

RADARSAT-1 C-HHStandard beam (S7)21-March-96θ = 45° - 49°Res.: 20 m (Rg) x 27 m (Az)Pixel spacing : 32 m look direction

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Siltstone : 1.7 cm

Limestone : 4.6 cm




Effect of incident angle on backscatter

Incident angle

beam mode

‘smooth surface’(siltstone)

‘rough surface’(carbonates)

β°R

adar

bac

ksca

tter (

dB)

BATHURST ISLANDCalibrated RADARSAT-1 Data

Backscatter variation with angle of incident(fossiliferous carbonates vs. siltstone)




Desert Environments

• Sparse vegetation

• Dry soil conditions

• Pebble size of alluvium strongly affects backscatter

• Backscatter mainly controlled by soil moisture and surface roughness

• If possible, avoid precipitation events


Lithology in desert

environments

Lunar Lake Volcanic Field, NEVADARADARSAT-1 Fine Mode F4 18-Oct-96

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Resolution : 7.8 m (Rg) x 8.4 m (Az)Display pixel spacing : 25 m

Orb

it

Look

Incident angle : 45º -48º

D'Iorio M. , B. Rivard, P. Budkewitsch, 1996



High and low surface

roughness

LUNAR LAKE

Nevada, USA

LAVA FLOW

Nevada, USA


Morphology in desert

environments

1997

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ZAGROS FOLD BELT, IRANRADARSAT-1 11-Nov-97

ScanSARNarrow B

Incident angle31º - 46º

NominalResolution50 m x 75 m

(Rg x Az)

Pixel Spacing160 m


Data Integration• SAR imagery may be used as a basis for data

integration.

• Any properly geocoded digital dataset can be integrated with the SAR imagery.

• The resulting integrated product has a greater information value than the sum of the information of the individual constituents.

• Techniques such as IHS, Addition, Multiplication, Principal Component Analysis, etc. can be used to merge the datasets.

• With the IHS technique, the SAR imagery is used to modulate intensity, while the merged dataset is used to modulate hue.


Data integration

(soil geochemistry and SAR)

Nickel in soil (0-16 ppm Ni)

Airborne C-SAR and Soil Geochemistry


Airborne Synthetic Aperture Radar C-HH

SAR + Geochemistry IHS integration

Source: Singhroy V. , R. Saint-Jean, B. Rivard 1995.



Data integration(geological map

and SAR)

Data Integration and Interpretation

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.W.T

.

Source: Paul Budkewitsch, Marc A. D’Iorio, and J. Chris Harisson. 1996.



Data integration(Optical imagery

and SAR)

AirborneC-SAR

C-SAR and Landsat TM

Landsat TMPCA (TM4,5,7)

Azraq, JordanData Integration

Source: Singhroy V. , R. Saint-Jean, B. Rivard 1995.



RADARSAT Stereo Image Pairs

• Appropriate RADARSAT image pairs can be viewed stereoscopically to provide a three-dimensional perspective of terrain landforms

• Stereo pairs have proven useful for terrain mapping and DEM generation

• Subtle features not discernible in single RADARSAT images are often recognized in stereo pairs


Selection of Stereo Image Pairs• Best results obtained from same-side (i.e.,

descending/descending or ascending/ascending) image pairs with large overlap

• Opposite-side (i.e., ascending/descending) image pairs only recommended for very low relief areas; similar tonal characteristics

• Preference for one image with a large incident angle (i.e., S7 or EH1-6) to minimise terrain displacement effects

• The larger the difference between incident angles, the greater the vertical exaggeration in the stereo pair– high relief : 5°- 20° is sufficient– low relief : 20°- 40° is required


Stereo image pair

MULTI-ANDEAN PROJECT, BOLIVIAStereo Image Pair

98-Aug-23 S3 Desc 97-Mar-27 S6 Desc


RADARSAT-1

Display Pixel Spacing : 123 mSub - scene

Descending pass (right looking)

Source: Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu,C.W. Lee, 1999



DEM produced using

radargrammetryand

RADARSAT-1image pair

Digital Elevation Model of Multi-Andean Project of Bolivia

Standard Image Pair : 98-Aug-23 (S3, Desc) & 97-Mar-27 (S6, Desc)

Source: Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu,C.W. Lee, 1999



RADARSAT-1perspective

image

Perspective Viewing ImageMulti-Andean Project, Bolivia

Ortho colour image (IHS) draped over DEMPixel Spacing = 25 m


Source: Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu, C.W. Lee, 1999



Geological Applications

• SAR can provide information for :

– Geological mapping: regional surveys, map updating, geomorphological mapping, structural and tectonic interpretation

– Mineral exploration: provides simultaneous interpretation of information coming from several datasets

– Geological hazards mapping: The all weather capabilities and the sensitivity to surface morphology provides information on remote areas


Map updating, Regional surveys,

Structural interpretation

Geological mapping

1996 C

anadian Space Agency

RAD

ARSAT-1 S6 (descending)

(west looking

Resolution : 21 m (Rg) x 27 m (Az)Pixel Spacing: 50 m

MACRES/CCRS

Sarawak, MalaysiaGeological Map (Yin, 1992) Structural (stereo) Interpretation

Source: D'Iorio M. , P. Budkewitsch, N.N. Mahmood, 1997.



Mineral exploration

SAR providesgeomorphologicalinformation while the other dataset gives additional

information

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/geology/sudbury/sudburye.html

Granite

Greenstones and Sedimentary Rock

Granite and Granite Gneiss

Sudbury

LEGENDMining Properties

Sudbury mining district, OntarioRADARSAT-1 and Magnetics (VG) Integration

MagneticsVertical Gradient of the magnetic fieldFrom GSC Airborne SurveyLine Spacing: 500 mIntegrated through IHS with:Intensity : RADARSAT SARHue : Magnetics VGSaturation : Constant (DN=65)

RADARSAT-1Orbit 3043, 1996-June-04STANDARD Mode, Beam 1Resol.: 26 m (Rg) x 27 m (Az)Pixel Size Approx. 39 m x 39 mInc. Angle: 20º - 27ºSub-image



Nickel Irruptive

Oneping Fm

Chelmsford Fm

WanapiteiLake


Geological hazards mapping

SAR providesgeomorphological

information

YALE LANDSLIDEFraser Valley, B.C.



SAR provides information

about remote areas

Geological hazards mapping

Asc

endi

ng P

ass,

righ

t loo

king

Pixel Spacing = 12 mSub-scene


Nevado Del Ruíz, ColombiaDec. 1, 1998, RADARSAT-1 Beam F2

Landslide


RADARSAT and Geological Mapping

• Topographic relief is the main factor for selecting beam position (incident angle)

• Preference of radar look-direction (ascending vs. descending) to be close to orthogonal to the principal trend of the bedrock structure ; often the most compelling factor for choosing between ascending or descending images

• Viewing stereo image pairs significantly improves interpretation of geological structures (i.e., folds and faults)


RADARSAT and Geological Mapping (continued)

General guidelines :• Low to moderate relief (100-500 m) : all Standard

beam modes (application dependent) ; moderate preference for S1 to S5 for revealing terrain detail.

• High relief (1000 m) : highest incident angles are best (i.e., S5-7). EH1-EH6 beams also recommended to minimise terrain displacement effects, however shadows may result

• F1 to F5 in all cases exhibit few differences


RADARSAT and Geological Mapping (continued)

Regional Studies :• ScanSAR Narrow (SN1, SN2) or ScanSAR Wide

(SW2) useful for wide-area mosaics

• Information content in Wide mode (W1-W3) is similar to Standard mode (S1-S7) images

Detailed Studies :• Information content in all Fine modes (F1-F5) is

essentially the same

• Fine mode is recommended from 1:20 000 - 1:50 000 or smaller scale image maps


HydrologyApplications


Hydrology Applications

• Soil Moisture• Wetlands Mapping• Flood Mapping• Snow Mapping• Hydrological Modelling


Soil Target Parameters

• Water content (complex dielectric constant) of surface layer

• Penetration depth depends on soil moisture content of soils, frequency and incident angle

• Surface roughness (usually tillage related and measured using two parameters: rms surface height and correlation length)

• Surface macro-structure (ie. tillage row characteristics, tillage direction and seed bed structures)


Soil Moisture Mapping

• Quantify surface (0-15 cm) soil moisture

• Identify moisture spatial variability

• Extrapolation of surface moisture to root zone for yield estimation

• Crop stress assessment and input to growth models

• Irrigation scheduling

• Flood forecasting

• Climatological and hydrological models


Soil Moisture Sensitivity

Ls = 5 cm, s = 0.5 cm Small perturbations model used (for smooth surfaces)The two surface roughness parameters are:s = rms surface height (cm) (vertical character of the soil surface)Ls = large-scale correlation length (cm) horizontal character of the soil surface)

Reference: Touré, A., K.P.B. Thomson, G. Edwards, R.J. Brown and B.Brisco, 1991

Cvv CHH

σ°dB

Incident angle (θ) degreesIncident angle (θ) degrees

σ°dB

Results from the Université Laval obtained with the Michigan Microwave Canopy Scattering Model (MIMICS)

mv = 0.05mv = 0.15mv = 0.25mv = 0.35mv = 0.45mv = 0.55

Volumetric soil moisture

content (g/cm3)


Surface Roughness Sensitivity

Ls = 5 cm, mv = 0.1 cm Small perturbations model used (for smooth surfaces)mv = volumetric moisture content (g/cm3) The two surface roughness parameters are:s = rms surface height (cm) (vertical character of the soil surface)Ls = large-scale correlation length (cm) (horizontal character of the soil surface)

Reference: Touré, A., K.P.B. Thomson, G. Edwards, R.J. Brown and B.Brisco, 1991

s = 0.5 cm s = 1.5 cms = 2.5 cms = 3.5 cms = 4.5 cms = 5.5 cm

RMS surface height

Incident angle (θ) degrees

Cvv CHH

Incident angle (θ) degrees

σ°dB

σ°dB

Results from the Université Laval obtained with the Michigan Microwave Canopy Scattering Model (MIMICS)


Wet conditionsDry conditions

RADARSAT ImagesStandard Mode Beam 2

May 5, 1996 May 15, 1996

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QUANTITATIVE SOIL MOISTURE ESTIMATION

Source: Pultz, T.J., Y. Crevier, B. Brisco, R.J. Brown and Q.H.J. Gwyn, 1997. Soil Moisture Estimation with RADARSAT; Proceedings, International Society for Optical Engineering (SPIE), 22-25 Sept. 1997, London, UK, p. 143-148


Regressionand 95%

confidence interval

Quantitative Soil Moisture EstimationO

bser

ved

σ°

Valu

es

Predicted σ ° Values

Regression of RADARSAT Backscatter corrected for Incident Angle

versusSoil Moisture in the top 3 cm

Regression coefficient = 0.88

Date Variable* RRange of

observed values Incident Angle05-May-96 0-3 cm Mv 0.83 19-50% (31%) 29

0-5 cm Mv 0.82 19-42% (23%) 290-10 cm Mv 0.84 21-47% (26%) 290-3 cm Mv,

& RMS 0.91 11-20 mm 2915-May-96 0-3 cm Mv 0.27 22-35% (13%) 26-27

0-5 cm Mv 0.49 18-29% (11%) 26-270-10 cm Mv 0.4 24-34% (10%) 26-270-3 cm Mv,

& RMS 0.39 9-19 mm 26-27May 5 and 15 0-3 cm Mv 0.88 19-50% (31%) 26-29

0-3 cm Mv, & RMS 0.83 9-19 mm 26-29

* Single (Mv) or mulitiple (Mv and RMS) variable regressionMv = Volumetric soil moisture content (%)

in top 0-3 cm, 0-5 cm or 0-10 cm of soilRMS = Root mean square height [Measure of surface roughness]R = Regression coefficient

Carp River Watershed May 5, 1996 Asc. and May 15, 1996 Desc.

The strongest relationship between radar backscatter and soil moisture was observed with the 0-3 cm volumetric soil moisture. Regression coefficient increased from R=.66 to R=.88, after the inclusion of a first-order correction for incident angle.

Source: Pultz, T.J., Y. Crevier, B. Brisco, R.J. Brown and Q.H.J. Gwyn, 1997.


C-band Regression Results(Backscatter Versus Surface Soil Moisture and Roughness)

Data from CCRS ground-based scatterometer

* significant at p < 0.05source: McNairn et al., 1996

Polarization RegressionModel Type Model Variables θ R*

HH Simple Soil moisture (0-2.5 cm) 20º 0.6350º Not significant

Simple Roughness 20º 0.3850º 0.90

Multiple Soil Moisture and roughness 20º 0.7750º 0.93

VV Simple Soil moisture (0-2.5cm) 20º 0.5950º 0.33


Multiple Soil moisture and roughness 20º 0.7550º 0.88

HV Simple Soil moisture (0-2.5cm) 20º 0.6250º Not significant


Multiple Soil Moisture and roughness 20º 0.9150º 0.83

Source: McNairn et al., 1996 bare soil *significant at α < 0.05


Soil Moisture MappingRecommended Radar Configurations

• Incident angle• Steeper angles minimize roughness contributions• Correction factor required when combining data sets with

different incident angles

• Imaging mode• Wide, Standard and Fine modes are most suitable for field

based information• ScanSAR provides regional moisture estimates


Soil Moisture MappingImplications for Data Acquisition

• Timing• Soil moisture conditions are very dynamic and timing of

acquisitions is very important, particularly if tied with field measurements

• Ascending and descending acquisitions will have different temperatures and moisture conditions

• Environmental effects• Obtain climate data to determine environmental conditions

at time of overpass• Moisture content of frozen soil cannot be measured with

microwaves • Penetration depth

• Depends on moisture conditions and incident angle• Important to understand penetration depth for modelling purposes


Wetlands Mapping

• Canopy architecture important in backscatter

• Corner reflector effect between underlying water and vegetation important in wetland discrimination

• Wetlands...

• Improve water quality and groundwater recharge

• Support a diverse wildlife habitat and unique vegetation

• Maintain a balanced hydrological system

• Act as indicators of environmental health


Rivière des Outaouais

Mississippi River Flood April 7, 1998RADARSAT F5F Incident Angle: 45.6° - 47.8° Ascending

Data received and interpreted by the Canada Centre for Remote SensingProcessed and distributed by RADARSAT International 1998 Canadian Space Agency


Wetlands Mapping

Recommended Radar Configurations• Incident angle

– Angles in the mid-range are best to ensure that both direct canopy and canopy-water interactions are detected

• Imaging mode– Most wetlands require only local coverage– Standard and Fine modes provide most detail

Implications for Data Acquisition• Environmental effects

– Rain and dew affect absolute backscatter and can significantly degrade contrast among targets


Flood Mapping

• Specular reflection from water produces dark return• Corner reflector effect highlights flooded vegetation

Applications in flooding monitoring• Mapping flood extent and duration • Monitoring of lands flooded throughout the flood period• Flood damage assessment• Mapping inundated vegetation


Flood MappingRecommended Radar Configurations

• Imaging mode• Wide, Standard or Fine for local area coverage• ScanSAR for regional coverage

• Incident angle• Shallow angles provide best contrast between land and

water (water surface becomes more specular and these angles enhance roughness associated with land surfaces)

• Intermediate angles are a good compromise when mapping surface water and flooded vegetation


Flood MappingRecommended Radar Configurations

• Timing• Extremely critical for mitigation activities and relief efforts• Less critical for refinement of prediction models and damage

assessment.

• Environmental effects• Wind can increase backscatter from flooded surfaces due to

increased roughness• Wet snow appears very dark and can cause confusion with

flooded areas


Flood Monitoring Red River, Manitoba Spring 1996

A During the acquisition on March 23, the site was covered by a thick layer of snow. The air tempteraturewas below the freezing point creating dry snow conditions transparent to the microwave. The ground was also frozen during the acquisition, which explains the lack of contrast between features on the image.

B The combination of above normal snow precipitation and late spring runoff created optimal conditions for the development of a flood event on the Red River. The flood extent on the April 25 and May 9 images is identified by the darker tones. The very bright features are classified as flooded standing vegetation.

March 23, 1996 April 25, 1996 May 09, 1996

RADARSAT S3 descendingRADARSAT S1 descendingRADARSAT S1 ascending

Source: Pultz, T.J., Yves Crevier, 1996.

1996 C


1996 C



Source: Pultz, T.J., Yves Crevier, 1996. "Early Demonstration of RADARSAT for Applications in Hydrology". Third International Workshop on Applications of Remote Sensing in Hydrology, Greenbelt, Maryland. October 16-18, pp.271-282.

C A color composite image was created using the May 9, April 25 and March 23 images. This RADARSAT colorcombination is showing the evolution of the flood during a 2 week period. The dark blue tones are classified as the common flood area on the two dates. The red features, located on the west side of the Red River, are identiifed as the flooded area on the early date. The blue/green surfaces, north of Morris, are flooded areas only on the later date. The yellow rectangle is the levee-protected town of Morris, located approximately 60 km south of Winnipeg. This example demonstrates the strong potential of RADARSAT for flood extent mapping, damage assessment and flood monitoring.

Flood Monitoring Red River, Manitoba Spring 1996


Reference: Pultz T. J. and Crevier Y., 1996 Estimation of SnowAreal Extent Using RADARSAT Data

http://www.ccrs.nrcan.gc.ca/ccrs/comvnts/rsic/2401/2401ap4e.html

DRY SNOW COVER

Hydrology Applications Laboratory

RADARSAT-1 imageDate: January 19, 1996Acquisition: 17:07 local timeRegion: Ottawa, On. CanadaBeam mode: STANDARDBeam postion: S6Incident angle: 41° - 46°Orbit: ASCENDING

RADARSAT-1 imageDate: January 12, 1996Acquisition: 17:07 local timeRegion: Ottawa, On. CanadaBeam mode: STANDARDBeam postion: S7Incident angle: 45° - 49°Orbit: ASCENDING

C


C


SNOW AREAL EXTENT DELINEATIONUSING MULTI-DATE RADARSAT DATA

WET SNOW COVER


Distributed Hydrological Model

Models simulate the following hydrological processes:• Interception• Precipitation• Snowcover and Snowmelt• Evapotranspiration• Infiltration• Groundwater flows• Interflow and baseflow• Overland and channel routing• Surface storage

Jobin, D.I., T.J.Pultz, 1996. "Assessment of three Distributed hydrological models for use with remotely sensed inputs". Third International Workshop on Applications of Remote Sensing in Hydrology, Greenbelt, Maryland. October 16-18, pp. 109-130.

TRANSFERFUNCTION

PRODUCTIONFUNCTION

PRECIPITATION

SNOW ON GROUND

EVAPOTRAN-SPIRATION

HYDROTEL Model - developed by the Institute de la recherche scientific - Eau, Quebec


Distributed Hydrological Models

• Distributed models use a finite element approach to conceptually represent the physical system. They already are used in other water resources disciplines (atmospheric & hydraulic modelling).

• Scalability, System (Ecology) Integration, Maximize informational content of spatially distributed datasets.


HydrologyRADARSAT Recommendations

Application Preferred Mode and Incident Angles Advantages Disadvantages

Soil Moisture S1 / S2 (<30o)Timeliness Linear Relationship

Calibration; Vegetation & Roughness

Land Cover & Wetlands Mid to ShallowGood Separability of Level 1 Land Cover Classes

Level 2+ Land Cover Classes more difficult (combining with optical data improves results)

Flood Mapping Time DependentTimeliness; Water Specular; Flooded Veg.; Sensitivity

Wind (Shallow); Vegetation (Steep)

Snow Mapping Steep (Wet Snow) Shallow (Dry Snow)

Timeliness Temperature


Applications in Land Use & Land Cover

Natural Resources Ressources naturellesCanada Canada


The way the people use the land is strongly influenced by the history and cultural characteristics of a region.

Land Use & Land Cover Applications

Carmen, Manitoba (Canada): Regular pattern of agricultural land use and land cover

Artigas Department (Uruguay): Irregular pattern of agricultural land use and land cover

RADARSAT multi-temporal colour composites

RADARSAT S7 R:Sept. 6, 1997 G:Feb 21, 1998 B:Mar 17, 1998RADARSAT Fine R:Jun 28, 1997 G:Jul 05, 1997 B:Jul 22, 1997



• Primary level mapping, for example: • forest, agriculture, water, urban, wetland, and barren land

• Monitoring of changes, for example:• changes along the fringes between urban and rural• deforestation and reforestation• disaster impact assessment

• Sources of information for environmental protection and natural resource management, for example:

• impacts of access roads in remote areas• encroachment into conservation areas• coastal erosion • unplanned colonization• construction on flood plains



Land use and land cover mapping and monitoring are required for many purposes, e.g.:

• local and regional planning • environmental impact assessment • distribution of disaster relief• compliance monitoring • monitoring the effects of climate change• policy development • wildlife management


Forest lands

Very short prairie

grasslands

Mature forest

Bushes and shrubs on damp soil D

escending pass(w

est-looking)


Display Pixel Spacing = 81 mSub Scene

TIERRA DEL FUEGO, CHILEFeb 12, 1997 RADARSAT-1 Beam S5

Land use and land cover applications~ Primary level mapping ~

Source: Castro Ríos, R., M. Espinosa Toro, 1999 http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/internat/glbsar2/imagery/chi/chi_1e.html


ROSARIO, ARGENTINAVisual interpretation of single date image

In the wetland complex, • water, flooded vegetation, wetland associations, and upland vegetation

In the urban area, • very bright returns, due to corner reflections which occur when the radar beam is orthogonal to the street direction

• variations in tone can also indicate differences in construction material and housing density

In the dryland agricultural areas • dark tones -> bare, dry fields such as pasture or harvested crops

• intermediate tones -> forage and grain crops such as wheat or soybeans

• bright tones -> broad-leafed high biomass crops like canola.

RADARSAT-1 Mode F1 acquired April 5, 1997

Land use and land cover applications~ Orientation effects (streets) ~

Source: Cotlier, C. G., A. Ravenna y M. F. Huisman 1999http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/internat/glbsar2/imagery/arg/arg_29e.html


Canada Centre for Remote SensingCanadian International Development Agency (CIDA) Canadian Space Agency, 1996

Kilometres

Coastal GuyanaRADARSAT-1 Beam S7 Asc. Apr.12, 1996

Source: Singhroy V., 1996


Barley

Alfalfa

Wheat

SoybeansCorn

CV-580 C-band SAR, South of Ottawa, 9 July 1998Linear Polarization Composite: Red = HH; Green = HV; Blue = VV

Agricultural Land Cover Mapping~ Multipolarimetric Data ~

Multipolarimetric data provides improved land use/cover monitoring capabilitieshttp://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/r2demo/demo5/oviewe.html


Geological Land Cover Mapping ~ Integration of RADARSAT and airborne

gamma-ray data ~

Source: Pedroso, E.C., B. Rivard, A.P. Crósta, C.R. De Souza Filho, F.P. de Miranda, 2001

The gamma-ray data provided information about near surface geology. Thetexture in the SAR image allowed detection of subtle features related to lithologic domains associated with known gold deposits. The RADARSAT image also enhanced structural features, most of which wereorthogonal to the radar illumination.

Potassium - K (%) Thorium - eTh (ppm) Uranium - eU (ppm)


Iguazú Falls Area(Argentina, Brazil and Paraguay) Brazil

Argentina

Paraguay

Multi-temporal data integration RADARSAT-1 S7 desc.

Red = May 1997Blue = May 1998 Green = May 1999

Image enhancement by CCRS; Images courtesy of RADARSAT International

C

anad

ian

Spac

e Ag

ency

, 199

7, 1

998,

199

9

Land use and land cover applications~ Change detection ~

Multi-temporal data integration highlights changes in land cover within a specific time period. Grey indicates no change. Colours in agricultural areas are related to crop rotation. Forest harvesting and differences in water levels among the three years were also detected.


Evaluation of the Impact of El Niño

Mangrove ecosystem,Tumbes, Peru

Land cover and land use map created by visual interpretation of the 1997 RADARSAT-1 image

Multi-temporal data integration -RADARSAT-1 S6 images acquired June 1997 and June 1998Red: brighter tone in 1997 image than in 1998 (changes related to flooding, salinity, sedimentation)Green: brighter tone in 1998 image than in 1997 (mainly changes in forest foliage) Black and white: no change

MangroveShrimp cultivation (Active)Shrimp cultivation (In-active)Arborescent matorralMatorralArbor. matorral - Cultivated landMatorral - PastureMatorral - Cultivated landCultivated land 1Cultivated land 2Saline areasPlayas and sand banksUrban areasWaterbodiesWallsAirport

Produced by : Lab. of Applications of Remote Sensing and GIS, Faculty of Forest Science, Universidad Nacional Agraria - La Molina Date: May 1999

Source: Huerta Sánchez, P., V.Barrena Arroyo e C.Garnica Philipps, 1999

Land cover and land use

Matorral = sclerophyllous scrub

Scale

Pacific Ocean


Impact in Honduras of Flooding Resulting from

Hurricane Mitch

Hurricane Mitch was one of the most destructive storms to hit Central America. It peaked on October 26 and 27, 1998 with sustained winds in excess of 280 km/h. On October 30, Mitch made landfall over Honduras. This image shows the flooding in theChamelecon River Basin after Hurricane Mitch. Roads

Rivers and Lakes

Flooded Areas

Flooded Vegetation

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/em/disasters/atmsphre/mitche.html

RADARSAT S2October 30, 1998 at 06:47 local time


Monitoring flood impact in China

Around PoyangLake, dykes were

build to hold back water and create farmland.

When the Yangtze River

flooded in 1998, water overflowed

the dykes and severe flooding occurred in this

area.

Area of Interest Peak Flood seasonFarms inside dykes

are flooded

Maximum flood level Chinese Airborne L-band SAR image August 2, 1998

Farms inside dykes

Pre-flood Landsat TM image April 3, 1998 566 lines x 1241 pixels

Source: Shao Yun, Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences. Beijing, China.


Landsat TM image April 3, 1998400 lines x 400 pixels

Landsat TM image Oct. 9, 2000400 lines x 400 pixels

Productive farms surrounded

by dykes(paddy rice fields)

Farms inside dykes are now flooded

and not in production

Monitoring land use change ~ Poyang Lake, China ~

Source: Shao Yun, Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences. Beijing, China.

The approach to management of the Yangtze floodplain, known as the “reconstruction principle”, encourages activities and land uses that will reduce future flood damage. Returning farmland to the Poyang Lake reduces potential crop losses. Also the lake’s role as a reservoir increases; more water is collected and stored and flood levels are reduced.



C

anad

ian

Spac

e A

genc

y 19

96

Agricultural Colonization

Changing Land Use

Boundaries of the areas cleared for settlement in can be used to infer land use change.

Source: Kux H.J.H. , J. R. dos Santos, F. Ahern, R. W. Pietsch, M. S. Lacruz, 1998

ProRADAR - Humaita Settlement, Acre, Brazil 98-May-15RADARSAT-1 Standard Mode Beam 5 (θ = 36°-42°) Resolution: 24.2m (Rg) x 27m (Az)


Land Use & Land Cover Applications ~ Recommended Radar Configurations ~

Beam Selection Considerations• Incident angle

• Large angles provide better discrimination (less soil contribution)

• At these angles, more interaction with vegetation occurs and information is provided on vegetation structure

• Imaging Mode (ScanSAR, Wide, Standard, Fine)• Depends on application and desired coverage

(local or regional). Always a trade-off between image resolution and swath coverage


Land Use & Land Cover Applications ~ Recommended Radar Configurations ~

Beam Selection Considerations

• Look direction • Orientation (streets) and row direction (agriculture)

effects can be significant at or near perpendicular look directions

• For crops, row effects are prominent at incident angles around 40o and at low vegetation densities


Land Use & Land Cover Applications~ Important Considerations for Image Acquisition ~

Acquisition Planning Considerations

• Environmental effects• Rain affects absolute backscatter and can significantly degrade

contrast among targets• Collection of meteorological data during and prior to acquisition

campaign will help identify these effects

• Select time of acquisition (ascending vs descending pass) to reduce the effects of dew and wind

• Select configuration taking into consideration vegetation type, distribution and phenological stage and their effects on backscatter


Land Use & Land Cover Applications ~ Important Considerations for Image Acquisition ~

Data Integration Considerations

• Change detection• Important to isolate non-target effects (differences in incident angle,

environmental effects, calibration effects …) to ensure that backscatter changes can be attributed to changes in the state of the target

• Multiple data sets• Multi-temporal data sets are often required• Differences in incident angle within a data set must be accounted for in

processing and analysis, particularly for extraction of quantitative values and for modeling,

• Integrating optical and SAR data can provide useful results


Mapping Applications


Mapping Applications

Outline• Orthorectification• Data fusion• Radargrammetry (Stereo)

See also “Applications of SAR Interferometry”


Orthorectification

• Background

• Mapping applications usingorthoimages


Background

• Mathematical modelling is needed to transform the original image into an orthoimage in the cartographic projection of the user.

• Mathematical model is the radargrammetric method (Earth geometry, platform, known sensor).

• Ground Control Points (GCPs) are used to precisely establish the transformation.

• Terrain elevation information is used for the orthorectification.

• Resampling algorithm is cubic convolution or an adaptive filter.


Topographic Displacement Radar Sensor

Source: T. Toutin, 1992, MOS and SEASAT Image Geometric Correction IEEE-TGARS, Vol. 30, No. 3, pp. 603-609.

θ

θ


mountain top

reference surface orthographicprojection ofmountaintop

airborne

satellite

radar ground rangeprojection of mountaintop

Horizontal displacement of a 100m mountain top (m)


Principle of Image GeocodingGrey value Interpolation (Resampling)

Radar Image

Grey Value Assignment Map to ImageTransformation

Digital ElevationModel

GeocodedImage


Georeferenced Images

Source: PCI Geomatics

Georeferencedimages

gridvector

map

imageimage

image


Data Integration and Fusion• Image Maps

• Integration of topographic features with an orthoimage

– example: Chicoutimi, Lac-Saint-Jean• Extraction of planimetric features from an

orthoimage (road lake, power line, railway etc.)• Data fusion

• Different sources data are registered [GIS, digital maps, grids and other orthoimages (VIR and SAR)]

– example: Charlevoix, Canada• Data fusion usually increases interpretability


3-D Image Map



Fusion of orthoimagesCharlevoix, Canada

Red: TM-C7 Green: TM-C4 Blue: ERS-1


Radargrammetry (Stereo)

• Stereo SAR

• Mapping applications using stereo SAR


Stereo SAR

• Stereo viewing reproduces the natural process of stereo vision

• Natural stereo process needs two images acquired from different positions

• Theoretical error modelling accounts for geometric error propagation and not radiometric image content

• Radiometric disparities have more impact on SAR than on VIR imagery

• Compromise has to be reached between geometric and radiometric disparities


Geometry - StereoExtreme Configurations

Opposite Side Same Side

Large geometric disparitiesLarge radiometric disparities

Small geometric disparitiesSmall radiometric disparities

SOLUTION


Radar StereoscopyGeneral Guidelines for DEM Extraction

Source : Toutin, IEEE-TGARS, 37(5):2227-2238, 1999

TerrainSlopes

Flat 0°-10°

Rolling10°-30°

Mountainous30°-50°

Radiometricdisparities Small Medium LargeGeometricdisparities SmallMediumLarge

CompromisesSame side, large intersection angle

Opposite side,small look angles

Same side, small intersection angle and

large look angles

StereoRADARSAT

Configurations

S1 asc - S1 desc S7 - S1 (asc or desc) S7 - S4 (asc or desc)

F1 asc - F1 desc F5 - F1 (asc or desc) F4 - F1 (asc or desc)


Evaluation of RADARSAT Stereo Results (manual image matching)

StereoPair Mode Resolution Look

AnglesIntersection

AngleType

Of ReliefLE9090% Bias Minimum

ValueMaximum

ValueF1 asc Fine 9m x 8m 37º - 40º Low 21m -7.2m -44.6m 42.6m

F5 asc. Fine 7m x 8m 45º - 48º

8º

Moderate 39m -5.5m -78.5m 70.7m

S4 desc. Standard 26m x 27m 34º - 40º Low 24m 7.8m -36.4m 53.8m

S7 desc. Standard 20m x 27m 45º - 49º

10º

Moderate 35m 1.4m -58.8m 74.9m

S7 desc. Standard 20m x 27m 45º - 49º Low 26m -1.4m -49.1m 46.6m

EH6 desc Extended 17m x 27m 57º - 59º

11º




13º

Moderate 37m 11.7m -43.0m 82.2m


EH3 desc Extended 18m x 27m 51º - 55º

15º


S7 asc. Standard 20m x 27m 45º - 49º Low 21m -2.4m -40.5m 36.4m

S2 asc. Standard 24m x 27m 24º - 31º

17º




22º


F4 desc. Fine 8m x 8m 43º - 46º Low 12m -5.6m -27.7m 21.8m

F5 asc. Fine 7m x 8m 45º - 48º

89º

Moderate 47m 11.7m -66.1m 109.7m

F4 filter Fine 8m x 6m 43º - 46º Low 14m -7.8m -30.0m 28.1m

F5 filter Fine 7m x 8m 45º - 48º

89º

Moderate 44m 6.6m -97.0m 114.3m

Toutin Th., 1999 Radar Stereo Pairs for DEM Generation RADARSAT for Stereoscopy;Geomatics Info Magazine International, Vol. 13, No 1, 1999, pp. 6-9


StereoPair

VerticalParallax

Ratio

Type ofRelief

LE9090%

Confidence

Bias MinimumValue

MaximumValue

Low 12 m -13.3 m -33.2 m 8.4 mModerate 36 m 4.2 m -39.6 m 95.0 m

F5-F1Same side

0.31

Entire DEM 25 m -1.1 m -89.1 m 95.0 mLow 44 m -18.9 m -89.4 m 57.5 m

Moderate 58 m -77.1 m -153.4 m -3.0 mS7-H6Same side

0.31

Entire DEM 85 m -55.9 m -270.0 m 142.1 mLow 24 m 25.8 m -16.1 m 58.6 m

Moderate 46 m -6.5 m -81.2 m 42.6 mS4-S7Same side

0.39

Entire DEM 45 m -1.3 m -126.0 m 150.3 mLow 23 m 11.7 m -101.7 m 42.0 m

Moderate 59 m -18.0 m -116.6 m 42.0 mS4-H3Same side

0.59


Moderate 29 m 10.9 m -23.0 m 66.6 mS1-S4Same side

0.97


Moderate 43 m -2.0 m -64.7 m 61.0 mS2-S7Same side

0.99


Moderate 27 m 6.6 m -32.0 m 65.6 mS1-S7Same side

1.37


Moderate 107 m -7.4 m -179.0 m 199.0 mF4-F5OppositeSide

1.97


Moderate 77 m -2.2 m -132.2 m 132.8 mF4-F5Opp. SideFiltered

1.97

Entire DEM 47 m -14.3 m -289.5 m 260.1 m

Evaluation of RADARSAT Stereo Results (automatic image matching)

Toutin Th. , A.L. Gray 2000, State-of-the-art of extraction of elevation data using satellite SAR data; ISPRS Journal of Photogrammetry and Remote Sensing , Vol. 55 , No 1 , 2000 , pp. 13-33


Stereo SAR Applications

• Stereo mapping (planimetry):• Extraction of map features: road, lake, urban, power line,

railway etc.référence : Th. Toutin, Potential of Road Stereo Mapping with RADARSAT Images, Photogrammetric Engineering & Remote Sensing, Sept. 2001, Vol. 67 no. 9, pp.1077-1084

• Digital Elevation Model of Pastos Grandes Caldera volcanic region, S.W. Bolivia

• DEM generation (S3 Desc. / S6, Desc.) – example: DEM, Bolivia

• Contour line creation– example: DEM with contours, Bolivia

• Analysis and interpretation• Perspective viewing

– example: perspective image, Bolivia


Examples of DEM Applications

• Chromo-stereoscopic images (Intensity/Hue/ Saturation colour space and 3-D viewing)

• a relief image with colour-coded elevations. The colours were derived in the IHS colour space and mapped to Red/Green/Blue (RGB) colour space

– example: chromo-stereoscopic image, Bolivia

• Perspective-view images • orthoimage draped over Digital Elevation Model

– example: perspective image, Bolivia

• Digital Elevation Model from Fine mode RADARSAT images of Espíritu Santo Region, Bolivia

– example: stereo image pair, chromo-stereoscopic image


Stereo Image pair: 98-Aug-23 (S3, Desc) & 97-Mar-27 (S6, Desc)Kilometres

Miles

Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu, C.W. Lee, 1999


Digital Elevation Model of Multi-Andean Project, Bolivia

Relative elevationsMin. = 350 mMax. = 3100 m


Contour Interval 100 MetresKilometresMiles

DEM with Contour Overlay of Multi-Andean Project, Bolivia



Pixel Spacing = 25 m

Descending Pass (w

est looking)

I: 97-Mar-27 (S6, Desc) H: DEM S: CONSTANT (gray value: 150)IHS = RADARSAT & DEM

Chromo-Stereoscopic ImageMulti-Andean Project, Bolivia


Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu, C.W. Lee, 1999

http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/papere.cfm?BiblioID=4734 1997 Canadian Space Agency

Ortho colour image (IHS) draped over DEM

Perspective Viewing ImageMulti-Andean Project, Bolivia

Pixel Spacing = 25 m


RADARSAT -1 Stereo Image Pair~ Espíritu Santo, Bolivia ~

97-Jul-25 F2 Desc. 97-Jun-14 F5 Desc.

Descending Passes (right looking)

→

1997 Canadian Space AgencyArea of interest: 16°55’S 65°50’W and 17°15’S 65°25’W


IHS - RADARSAT & DEMI: RADARSAT

H: DEMS: CONSTANT

(Gray value: 150)

Pixel spacing: 6.25 m

DEM generated by

Technologies Ltd.

Descending Passes (right looking)

→


Chromo-stereoscopic Image~ Espíritu Santo, Bolivia ~

N

350 m

3100 m

Metres above sea level

1725 m


More Examples of DEM Applications

• Digital Elevation Model, Coclé Region, Panama, generated with PCI OrthoEngine software using satellite orbital data and ground control points acquired from 1:50,000 scale topographic maps

• RADARSAT stereo image pair• Digital Elevation Model• Chromo-stereoscopic image• Perspective view

• Digital Elevation Model, Colombia, produced usingIntermap Technologies’ SATMAP software technology

• Orthorectified image• Chromo-stereoscopic image


RADARSAT-1 Image S4 Asc, 05-May-99 RADARSAT-1 Image S6 Asc, 08-May-97

RADARSAT stereo image pairCoclé Region, Panama

GlobeSAR-2 Panama project Juan de Dios Villa MataDepartamento de Exploraciones Geologico-Mineras Direccion General de Recursos Minerales


Digital Elevation Model

Coclé Region,Panama

Produced fromstereo image pair08-May-97 (Asc, S6)05-May 99 (Asc, S4)


N

Chromo-stereoscopic image~ Coclé Region, Panama ~

Intensity: May 8, 1997 RADARSAT imageHue: DEMSaturation: Constant (150)


Perspective view ~ Coclé Region, Panama ~

0 630 1260

Metres above sea level


RADARSAT-1 orthorectified imageManizales Region, Colombia

June 22, 1997 (S7, Asc)Second Image : 24 Aug. 1997 (S4, Asc)

Chromo-stereoscopic imageDEM - 50 m postings

DEM & IHS generated by

Technologies Ltd.

GlobeSAR-2 project COL10 - Jaime E. Jaramillo Echeverri, Gustaveo A. Ochoa Villegas, Centro de Estudios Regionales Cafeteros y Empresariales (CRECE) and Olga P. Bohorquez, Maria L. Nomsalve, INGEMINAS

http://www.intermaptechnologies.com/html/mapp%5Fsatmap.htm

SATMAP Orthorectified Image~ Volcano del Ruíz, Colombia ~


SAR Ocean Imagingand Applications


SAR Ocean Imaging and Applications

Outline• Ocean-SAR interaction• Ocean SAR applications

• Ship detection• Oil spill and natural slick detection• Extraction of wind and wave speed and direction • Mapping of mesoscale ocean features• Mapping of atmospheric processes• Mapping coastal zones

• Suggested RADARSAT beam modes • Complementary ocean sensors


Ocean - SAR Interaction

• SAR backscatter influenced entirely by ocean surface roughness -- no radar penetration

• Backscatter strongly related to SAR incident angle and wind speed / direction

• Wide range of backscatter levels for ocean surfaces• e.g., -40 dB < σ° < + 10 dB


Ocean - SAR Interaction (continued)

• Bragg scattering model often used to describe SAR scattering over oceans

• appropriate for intermediate incident angles (approx. 20º-60°)

• describes resonant scattering from waves on the order of the wavelength of the radar (C-band ≈ 5cm)

– at C-band, these are commonly wind generated

• Higher wind speeds typically increase ocean surface roughness, which increases backscatter

• Ocean backscatter typically decreases with increasing incident angle


Hybrid Ocean Backscatter Modelbased on CMOD_IFR2

Source: Gray, A.L, P.W. Vachon, C.A. Bjerkelund and M.J. Manore, GER’97

σ0(d

B)

Incident Angle (deg)

Noise floor

CVV; phi=0 deg; U=10 m/s

CVV; phi=0 deg; U= 4 m/s

CHH; phi=0 deg; U=10 m/s

CHH; phi=0 deg; U= 4 m/s


West Coast of Vancouver.RADARSAT ScanSAR Narrow(Near Range Portion of Image)Aug. 3, 1996

Effect of Incident Angle

on Ocean Backscatter

Nea

r Ran

ge



• Backscatter is influenced by direction of the wind• higher backscatter when the radar looks in the same direction

as the wind (upwind, downwind)• lower backscatter when radar looks across the wind direction

• Variations in wind speed modulate the roughness of the Bragg scale surface waves → results in local changes in backscatter

• Detection of ocean features decreases with high seastates due to higher level of clutter


Fig. 1: Large incident angle with low wind speedθ = 47.3°, σ° = -27, U = 2 m/s

Fig. 2: Small incident angle with low wind speedθ = 23.9°, σ° = -9.6 dB, U = 5 m/s

Fig. 3: Large incident angle with higher wind speedθ = 43.8°, West of Front σ° = -16 dB, U = 11 m/s

East of Front σ° = -24 dB


Fig. 2 : 12 August 1997 02:00 UTC W1 Asc.Fig. 1 : 22 July 1997 14:21 UTC S7 Desc.

Fig. 3 : 8 August 1997 14:26 UTC S6 Desc

RADARSAT-1: West Coast of Vancouver Island (N48.6° W125.4°)

Vachon P. W. and R. Olsen, 1998



• Bragg Scattering is modulated by three principal mechanisms that can enhance or suppress average backscatter of ocean surface:

• tilt modulation– change in local incident angle

• hydrodynamic modulation– alteration of Bragg scale waves due to surface currents

• damping by surfactants– suppression of Bragg scale waves


Ocean SAR Scattering

BRAGG SCATTERING

RADAR SIGNAL

MOVING FACETS

WINDSHORT WAVES ON A LONG WAVE

BRAGG SCATTERING WHEN:2x = 2L SIN θ = n λ, n = 1, 2, 3…

TO SATELLITE

SURF

ACE

X = L SIN θ

Source: NASA



• Additional influence on ocean backscatter is “Velocity Bunching”

• artifact of SAR system caused by moving ocean surface• moving waves introduce Doppler offsets and result in azimuth

displacement ‘errors’ in images• displacements can combine in non-linear fashion and cannot

be removed• most prevalent for azimuth travelling waves

• Velocity bunching does not change average backscatter; it introduces only local variations due to location displacements


Velocity Bunching Illustration

Orbital Velocity

“Linear”

“Non Linear”

u + ve u - ve

Source :Vachon P. W., J. W. Campbell, et F.W. Dobson, 1999


Ocean Features - Scotian ShelfRADARSAT 1 Beam W1 Desc. March 30, 1996

Kilometres CSA 1996


Ocean SAR Applications

SAR is capable of providing operational information for:• Ship detection• Oil spill and natural surfactants monitoring• Extraction of wind and wave vectors (speed and direction)

– forecast models – search and rescue – oil spill clean-up

• Ocean mesoscale features – circulation models– search and rescue– fisheries resource management– oil spill clean-up

• Detection of marine atmospheric boundary layer phenomena• Mapping coastal zone features and processes


Ship Detection

• Ship detection involves the identification of point targets in a radar background

• ships are bright point targets in an ocean clutter background

• Detection dependent on • sea state• incident angle• vessel size, orientation, speed, etc.


Ship Detection (continued)

• Sea State• at high sea states:

– ocean clutter increases and ship detection is reduced• C-HH has lower clutter signature than C-VV

– for similar wind + wave conditions + resolution → C-HH is better than C-VV

• Incident Angle• ocean clutter is lower with increasing incident angle• detection improves with increasing incident angle• greater contrast is due to higher signal-to-noise (ship-to-

clutter) ratio

• Vessel length, speed and orientation • radar cross-section of vessel is affected by these parameters• fishing vessels are more difficult to identify due to size


RADARSAT and Ship Detection

Relative comparison of RADARSAT beam modes for ship detection as a function of incident angle.

Source: Vachon P. W., J.W. Campbell, C. Bjerkelund, F.W. Dobson, M.T. Rey, 1997

83 m Coast Guard ShipStandard 3

Ship

Ocean

ShipWind speed = 10 m/s

FOM

(m)

Incident angle (deg)

Ship Detection Figure of Merit



RADARSAT-1 Beam Mode: S5 26-03-96

Scotian ShelfShips Detected by Ocean Feature Workstation

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/iceocn/rsatship/shipe.html

Source:Chunchuzov I., P.W. Vachon, X. Li, 2000


Standard 3Wind speed 11.2 m/s from 92 degreesWave Heigth 1.9 m

March 20, 1996

Canadian Coast Guard Ship “Parizeau”

RADARSAT Ship Detection Field Experiment off Halifax


65 m1360 tons

“Parizeau” ship Profile


RADARSAT ImageStandard 5 Acquired: Oct 07, 1998 Collocation Product

Zoom-in of Collocation Product

Fish Factory Trawler


RADARSAT Subimage

Longitude (degrees)

Latit

ude

(deg

rees

)

Legend

M008671, 07OCT1996 02:13:08.41000

M008671, 07OCT1996 02:13:08.41000

Latit

ude

(deg

)

Legend

Longitude (deg)

Ocean Monitoring Workstation (OMW) Case Study

Vachon, Thomas, Cranton, Edel, Henschel, 2000



Beam Mode Images ValidatedPositive Negative Detection

Rate

Overall 27 174 34 84%

Least Favorable(S1-3,W1, W2) 13 95 28 77%

ScanSAR Narrow Far 2 17 4 81%

Recommended(F1-5, S4-7, W3) 12 62 2 97%

Validation statistics

Summary detection statistics for Ocean Monitoring Workstation (OMW) ship validation study

Source: Vachon, P.W., S.J. Thomas, C.J. Cranton, H.R. Edel, and M.D. Henschel, “ Validation of Ship Detection by the RADARSAT Synthetic Aperture Radar and the Ocean Monitoring Workstation”, Canadian Journal of Remote Sensing, Vol. 26, No. 3, 2000, pp. 200-212.



• Smallest vessel for which detection is validated • 20 m vessel has been detected in RADARSAT Standard 7

(wind speed = 4 m/s)

• Manual and automatic detection techniques possible• Preferred RADARSAT modes:

• W2, W3• S4-S7• F1-F4• EH1-EH6

• Detection of ship wake also possible with SAR, depending on

• ship size and speed• wind conditions• angle of incidence• etc


Oil Spill and Natural Slick Detection

• Surfactants cause localized suppression of Bragg scale waves

• SAR can identify location of oil spills and map their extent

• cannot determine oil slick thickness• difficulty in distinguishing between oil and “look-alikes”,

e.g., areas of low wind, grease ice and natural surfactants• detection optimum in moderate wind conditions

– 3m/s - 10m/s


RADARSAT and Oil Spill Detection

• Since launch, RADARSAT has been effectively used in the monitoring of several oil spills around the world

• Frequent revisit time invaluable for monitoring movement and dispersal of spill

• Preferred RADARSAT modes: • ScanSAR Narrow Near • S1-S4• W1

• Automatic detection and mapping of oil spills in SAR imagery shows promise

• look alike targets still a difficulty in most algorithms, therefore, only identification of ‘candidate’ oil spills is provided


Canadian Space Agency, 1996Image Courtesy of RSI

Full Swath - Pixel Spacing: 78 m

RADARSAT-1 22-Feb.-96

Beam Mode S1 (θ = 20° - 27°) C-HH Resolution: 26 m (Rg) x 27 m (Az)

←

desc

endi

ng p

ass

http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.html Section 5.9.3

SEA EMPRESS OILSPILLMILFORD HAVEN, WALES


Full Scene- Display Pixel Spacing: 113 m

RADARSAT-1 31-July-96Beam Mode W2 (θ = 31° - 39°) C-HH Resolution: 26.6 m (Rg) x 27 m (Az)


IRVING WHALE SALVAGE OPERATION

Source: : Werle, Dirk, B. Tittley,E. Theriault, and B. Whitehouse, 1997

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/iceocn/irving/irvinge.html


Extraction of Wind and Wave Vectors from SAR Data

• Assists prediction of local drift• support search and rescue operations• oil spill monitoring

• Used as input to atmospheric and ocean circulation models

• weather forecasting• fisheries resource planning

• Steeper (smaller) incident angles are preferred: • S1-S3• W1


Extraction of Wind Vectorsfrom SAR Data

• Wind vector extraction is possible based on semi-empirical model

• e.g., HH polarization hybrid of CMOD_IFR2 model • predicts ocean backscatter (σ°) as a function of:

– wind speed – wind direction relative to SAR– SAR incident angle

• Procedure• measure backscatter (σ°) from the SAR image • invert model to extract wind speed• requires radiometrically calibrated data• wind direction must be known

– can be derived from SAR imagery or from atmospheric models


Extraction of Wind Vectorsfrom SAR Data (continued)

• Wind direction• from SAR image

– possible to detect wind direction in approx. 50% of images – use wave spectral signature of boundary layer rolls – subject of ongoing research– advantage: sensitive to local wind direction, greater local

accuracy

• from atmospheric models– advantage

• normally available several times/day– disadvantages

• coarse grid spacing, not sensitive to local variations (e.g. coastal areas)

• model output versus direct observation


Standard 3Wind speed 11.2 m/s from 92 degreesWave Height 1.9 m

March 20, 1996

Canadian Coast Guard Ship “Parizeau”

Wind Speed and Wave Height Experiment off Halifax


65 m1360 tones

“Parizeau” ship Profile


-64°30’ -64°15’ -64°00’ -63°45’ -63°30’ -63°15’ -63°00’ -62°45’ -62°30’ -62°15’ -62°00’

Longitude

43°25’

43°35’

43°45’

43°55’

44°05’

44°15’

44°25’

44°35’

44°45’

44°55’

45°05’

Latitude

Vent OMW Wind

Marine Environmental Data Service Dept. of Fisheries & Oceans

Satellite: RSAT-1Image Type: SGFMode: Single BeamBeam: S3 Date: 10:23:33 20-03-1996Filename: dt22364-01

SatlanticContact: [email protected]: 15:55:56 04-01-1999

xA1

xB1

C1D1

E1F1

G1H1

I1J1

K1L1

www.meds-sdmm.dfo-mpo.gc.ca/meds/Databases/Satellite/omw/

Products_e.htm

Ocean Monitoring Workstation

(OMW) Wind Product

Manore, M.J., P.W. Vachon, C. Bjerkelund, H.R. Edel and B. Ramsey, 1998


Extraction of Wave Vectorsfrom SAR Data

• Partial information on the ocean wave directional height spectrum can be retrieved from SAR images (i.e., a description of the energy distribution of the waves in terms of their components - direction and frequency or wavelength - is determined)

• The retrieval is often carried out using model spectra to derive a more complete wave spectrum

• The retrieved spectra may be assimilated into wave forecast models

• Starting point: a power spectrum is extracted from the SAR image using a Fast Fourier Transform (FFT)


Extraction of Wave Vectorsfrom SAR Data (continued)

• Detection of range travelling waves is limited by the resolution

• e.g. minimum detectable wave for RADARSAT ≈ 20m (Fine mode)≈ 50m (Standard mode)

• Image spectra have 180° ambiguity in wave direction• Ambiguity may be resolved by using multiple-look techniques


Wave Spectrum versus

SAR Image Spectrum

λ = 100 m

λ = 200 m

Azimuthcut-off

Wave Buoy SAR Image SpectrumR

AN

GE

AZIMUTH

Directionalambiguity

Source: Vachon, P.W., H.E.Krogstad and J.S. Paterson (1994)



Wave imaging limitations:• azimuth smearing reduces the effective azimuth resolution

– due to velocity bunching and the duration of the synthetic aperture

• azimuth smearing imposes a lower limit on the detectability of short azimuth travelling waves

– described as ‘azimuth cut-off’– a function of sensor-target range (R) and platform velocity (V)

- e.g. for RADARSAT (R/V > 115s) - azimuth cut-off ≈ 200m - e.g. for aircraft SAR (R/V ∼ 30s) - azimuth cut-off ≈ 50m

• swell is imaged best; wind seas may be distorted


• SAR image spectra may be inverted with guidance from wave spectra from models

• SAR spectra are best for swell (e.g. λ > 200m)• model spectra are best for wind seas (e.g. λ < 200m) and

do not have a directional ambiguity• use an iterative approach to invert the SAR spectra

– use model spectra as first guess– blend the image and model spectra using a SAR model and

a weighting scheme– eliminates the 180° direction ambiguity– Hasselmann & Hasselmann (1991)



Extraction of Wave Vectorsfrom SAR Ocean Data

Note how the ERS-1 data have modified the strength of the swell system in the inverted spectrum. Azimuth is left to right and the circles represent 200-m (inner) and 100-m (outer) wavelengths. Vachon, P.W., H.E. Krogstad and J.S. Paterson (1994)

wave buoymodel

first-guess

model + ERSinverted spectrum

ERS imagespectrum

ERS forward-mapped spectrum

100m200m


RADARSAT Mapping ofMesoscale Ocean Features

• Modulation of sea surface roughness can permit the detection of mesoscale ocean features in SAR imagery

• e.g., fronts, eddies, current shears and internal wave patterns • modulation of roughness can be caused by:

– surface wind stress– wave to wave interaction and convergence

• Applications• circulation modelling• detection of upwelling areas for fishing• meteorological modelling• search and rescue• oil spill clean-up• coastal erosion/accretion studies.

• Low incident angle modes are best for detection• S1-S2, W1, ScanSAR Narrow Near



Descending Pass, R

ight Looking

South Atlantic Ocean - Argentina

Sept. 5, 1998, SCWRADARSAT-1Pixel Spacing = 58.4 m

Source : Gagliardini, D.A., J. Bava, J.A.Milovich, et L.A. Frulla, 1999


SAR Mapping of Atmospheric Processes

• Atmospheric stability• in an unstable atmosphere:

→ increase in atmospheric turbulence → increase in atmosphere-ocean friction

→ increase in short wave density → increase in radar backscatter

• Boundaries between stable (low backscatter) and unstable (high backscatter) atmospheric conditions can appear as brightness differences in RADARSAT ocean imagery


SAR Mapping of Atmospheric Processes (continued)

• Detection of storm cell activity • cells appear in SAR imagery as isolated, concentric dark areas• caused by wave-dampening effect of heavy rain and

downward turbulence• surrounding area has brighter returns due to higher outflow

winds

• Detection of boundary layer rolls• vertically moving air parcels in an unstable atmosphere cause

a detectable wave like pattern in SAR ocean imagery



AVHRR image courtesy of the University of Toronto, Department of Physics

30 Jan. 97 21:03 UTC

RADARSAT-1 ScanSAR Wide C-HH30 Jan. 1997 21:31 UTC

asce

mdo

mg

pass

LABRADOR SEA

Source:Chunchuzov I. , P.W. Vachon et B. Ramsay, 2000


Mapping Coastal Zones with SAR

• Coastal zones are highly dynamic regions with a diverse mix of land use and marine activity

• Many viewing options useful for mapping coastal zone features and processes at a wide range of spatial and temporal scales


Mapping Coastal Zones with SAR(continued)

• Human activities• coastal agriculture (e.g. rice)• fisheries (e.g. open water and farming)• land use planning

(e.g. monitoring urban sprawl, identification of beaches for tourism)

• environmental impact assessment

• Natural processes• erosion or accretion areas (e.g. shoreline change)• shallow water bathymetry (e.g. coral reef mapping)• intertidal vegetation (e.g. mangrove forests)• coastal zone sensitivity mapping (e.g. identifying at-risk

shorelines for oil spill response planning)


CoastalShrimp Farming Aguadulce, Panama

Dark ponds are flooded. Dry ponds are bright. Beams separating the ponds are visible.

RADARSAT-1 S6 Ascending PassMay 1, 1997Resolution: 12.5 m x 12.5 mSub-scene

Source : GlobeSAR-2 project PAN12 B. Cornelio LaraMinisterio de desarrollo agropecuario, DINAAC

GOLFO DE PARITA

Río Santa María

Río Estero Slado

HERRERA

COCLE


Ordering Coastal Zone RADARSAT Data

• If intertidal zone is focus, tidal range and schedule should be considered when ordering data

• Coastal zone areas where coastline is orientated along satellite path (approx. North-South) are susceptible to automatic gain control (AGC) effects

• When deciding between ascending or descending orbits, keep target of interest in the near range or use appropriate fixed AGC setting

• Higher incident angles (S6-S7, W3, F1-F5) provide best separation of land - water and more information regarding surface features → however, little detail water


Recommended RADARSAT Modes and Beams

SCW in brackets signifies that only a portion of the acquired swath may be suitable.

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/iceocn/beam/beame.html

Application Surveillance (location of the feature of

interest is not known)

Tracking (location of the feature of interest

is known approximately)

Slick Detection SCNnear, SCW S1-4, W1-2

Ship Detection SCNfar, SCW W3, S4-7, F1-5, EH1-6

Oceanic Features SCNnear, (SCW) S1-4, W1-2

Atmospheric Features SCNnear, W1, SCW

Ocean Waves S1, W1, SCNnear, (SCW)


Complementary Ocean Sensors• Ocean Colour:

• MOS-IRS (1996), SeaWiFS (1997), MODIS (1999), • chlorophyll, currents, fronts, eddies, ice concentration, upwelling, sea

surface temperature (SST)

• Scatterometers:• ERS-2 (1995), NSCAT (1996), QuickScat (1999)• wind direction and speed, ice edge

• Altimeters:• TOPEX/POSEIDON (1992), ERS-2 (1995), GEOSAT Follow-on

(1998)• current direction + speed, wave height, wind speed

• SARs:• ERS-2 (1995), JERS-1 (1992)• ice type + concentration + drift, oilslicks, ocean features, ship location,

wind speed + direction


Sea Ice SARApplications


Sea Ice SAR Applications

Outline• Ice information requirements

• SAR advantages

• SAR - sea ice interaction• First year ice

• Multi-year ice

• RADARSAT and sea ice mapping

• RADARSAT sea ice applications


Information Requirements• Interests in Sea Ice

• ship routing, navigation safety• marine engineering (e.g. ship design, bridges, oil platforms)• important component of aquatic ecosystem• effects on regional and global climate • possible indicator of climate change

• Ice information requirements• ice edge location• ice concentration• ice type (thickness)• age (first, second, multi-year)• floe size distribution• ice velocity• hemispheric ice volume


Sea Ice Mapping ~ SAR Advantages ~

• Microwave • penetration of clouds, fog• operational, reliable imaging

• Active• not restricted by low/no solar illumination at high latitudes

• High resolution/wide area coverage• SAR permits high resolution from satellite altitudes• 50km - 500km swath widths

• Information content• can distinguish between ice and open water

– ice edge, concentration• radar is sensitive to ice type, surface roughness

– age discrimination, ice topography


Aging Process of Sea-Ice

Aging Process of Sea-Ice at Mould Bay, N.W.T., October 1981 to June 1984(adapted from Bjerkelund et al., 1985)

Cum

ulat

ive

Ice

Thic

knes

s(m

)

bottom melt

snow Snow

D

epth

(m)

salinity (%)

surface 82/83

salinity (%) salinity (%)

snow

surface 81/82snow

FY ice

surface 83/84

surface melt

FY ice

FY ice

SY ice

growth meltYear 1

ice moves out of bay

Year 3Year 2melt growth growth melt


SAR - Sea Ice Interaction

• Ice is mixture of • ice• water• salt• brine (dissolved salt)• air

• Physical and chemical properties of ice change over time

• ice temperature• ice growth/decay (thickness, strength)• deformation

• Ice thickness• generally increases with age • SAR cannot directly measure ice thickness• thickness can be estimated by identifying ice type (age)


SAR Scattering~ First Year Sea Ice ~

• Large dielectric constant due to high salinity content • no penetration into the FY ice volume• sensitive primarily to surface roughness

• Smooth FY ice appears dark • specular reflection

• Rough/deformed areas appear bright• high surface roughness• multi-bounce

• Brightness of ice will evolve with changing surface characteristics

• e.g. from grease ice to pancake to smooth ice

• Ice signature affected by melt of surface snow cover


SAR - Sea Ice Interaction

First Year IceOpen Water

low salinitypenetration

volume scattering

Multi-Year Icehigh salinitylittle penetration

surface scatteringsensitive to roughness

no penetrationsurface scattering

sensitive to sea state

Low backscatter High backscatter


SAR Scattering ~ Multi-year Sea Ice ~

• Multi-year ice • experiences one or more melt seasons• lower salinity levels due to brine drainage• lower dielectric constant

• More penetration into volume of ice • scattering by air and brine inclusions in ice volume• scattering from both volume and surface• appears bright compared to undeformed first year ice

• Ice signature is affected by melt of surface snow cover


RADARSAT and Sea Ice Mapping

• C-HH radar well suited to ice mapping• Beam options permit local and regional mapping• Wide swath permit short revisit periods at high

latitudes (1-2 days) • Large incident angles preferred for

• ice/water discrimination• ice topography

• Analysis should include complementary sensors• optical/thermal (e.g. NOAA-AVHRR)• passive microwave (e.g. DMSP-SMM/I)

• Canadian Ice Service is one of the largest operational users of RADARSAT data


RADARSAT - Canadian Ice Service

1 -1 .5 hrs.

ISIS

CIDAS-COMM

Imagettes

Charts

Forecasts

FTP Phone

Clients

CellularINMARSAT1 - 3 hrs.

CDPF (Gatineau) MMO (CSA - St. Hubert)

RADARSAT

Products

Ramsay B. , M.J.Manore, L. Weir, K. Wilson, 1998


RADARSAT Sea Ice Applications

• Ice concentration (classification) maps• Route planning for ships in ice• Ice motion forecasting• Ice pressure forecasting• Iceberg detection• Ice climatology


Asce

ndin

g Pa

ss(r

ight

look

ing)

RADARSAT-1 ScanSAR Wide 96-Mar-06

Full swathDisplay Pixel Spacing: 250 m

1996, Canadian Space Agency

ICE DISTRIBUTION

Darkest tones -> open water

Greatest concentration of ice is north of PEI. As the ice moves around the islands, into the Gulf it thins and takes on a flowing appearance.

GULF OF ST. LAWRENCE, CANADA

PEI


http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/images/que/rque01e.html

RADARSAT Gulf Validation ExperimentScanSAR Narrow March 6, 1996

150 km sub-scene, 100 m pixel spacing 1996, Canadian Space Agency Image courtesy of RSI

tears (open water cracks)

pancake ice

Nilas (thin elastic crust of ice )

Nilas (thin elastic crust of ice )

brash ice

first year floes

pressure ridges

“crack" or "lead“

in first year floe

http://www.cis.ec.gc.ca/about/term.html

Canada Centre for Remote Sensing, Natural Resources Canadahttp://www.cis.ec.gc.ca/about/code.html

Bent Horn Oil Terminal

Bathurst Island

Polaris Mine

RADARSAT-1 96-Aug-11Beam SCW-A (θ = 20º - 49º) C-HH Resolution: 146.8 m (Rg) x 93.1 m (Az)

ICE ANALYSIS AND SHIP ROUTING

SubsceneDisplay Pixel Spacing: 300 m

Ice Analysis by Canadian Ice Service,Environment Canada 1996 Canadian Space Agency


RADARSAT-1 96-Feb-28Beam mode W2 (θ = 31º - 39º) C-HH Resolution: 26.6 m (Rg) x 27 m (Az)

ICE RECONNAISSANCE

Image courtesy of RSI 1996, Canadian Space Agency

Îles de la Madeleine,Gulf of St. Lawrence


RADARSAT-1 Gulf of St. Lawrence 96-Mar-06ICE MOTION PRODUCT

1996 Canadian Space AgencyImage courtesy RSI

Reference Image: ScanSAR Narrow A, DescendingMatch Image: ScanSAR Wide A, Ascending

http://www.noetix.on.ca/ - Auto Tracker

Canada Centre for Remote Sensing, Natural Resources Canadahttp://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/em/cchange/glaciers/iceberge.html

ScanSAR Wide B 98-11-01

Display pixel spacing: 300 m

Ronne Ice Shelf, AntarcticaRADARSAT Observes the Calving of Iceberg A-38

1997, 1998 Canadian Space AgencyImages received by CCRS and ASF (Alaska SAR Facility) Processed by RSI and ASF.Image courtesy of CCRS, CSA and NIC (U.S. National Ice Centre )


Source: Picasso, Manuel, H. Salgado, and B. Lorenzo,Monitoreo de hielo marino, Proceedings ofGlobeSAR-2 Final Symposium, May 17-20, 1999 p. 103-108

KilometersMiles

Miles

Iceberg

Iceberg

« Defense Meteorological

Satellite Program /

Operational LinescanSystem »

LCC, Clarke 1866 (NAD 27)

RADARSAT-1ScanSAR Narrow

19-01-99

WEDDELL SEA, ANTARCTICARADARSAT - DMSP/OLS

ScanSAR Narrow 99-01-19 01:10:39 UTC 1999, Canadian Space Agency Miles


Western Arctic / L'Ouest de l'Arctique

http://www.cis.ec.gc.ca/

Nautical Miles / Milles Marins

Kilometers / KilometresScale / Echelle


Applications of SAR Interferometry


Applications ofSAR Interferometry

• Overview of interferometry• Interfermetric SAR (InSAR) systems• Satellite InSAR (repeat pass principle,

geometry)• InSAR processing• Limitations of RADARSAT for interferometry• InSAR Applications

• Measuring topography • Measuring motion of the Earth’s surface

See also “Mapping Applications”


Radar Interferometry• Interferometry is the method of using two SAR images,

taken with a time delay and/or cross-track parallax, to infer height or motion information of the Earth’s surface.

• With a selected time delay and zero parallax, pure motion is measured.

• With zero time delay and a selected cross-track parallax, pure height is measured.

• Between most satellite image pairs, both time delay and parallax exist; therefore motion and height information must be separated.

• With airborne SAR, the time delay or the parallax between images is nearly zero, so near-ideal height or motion measurements can be achieved.http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/ana/interfer/interfre.html


Interferometric SAR Systems • Repeat-pass or repeat-track interferometry

• single antenna SAR systems which acquire images of the same scene from slightly displaced tracks (aircraft) or orbits (all spaceborne SAR systems to date, except SRTM). The ERS-1 / ERS-2 tandem mission was designed to demonstrate repeat-pass interferometry.

• Across-track interferometry• single-pass SAR systems with two receive antennas

displaced in the across-track plane (e.g., systems on the SRTM, the Convair 580 and the STAR-3i aircraft)

• Along-track interferometry• single-pass SAR systems with two receive antennas

displaced in the along-track direction, used to measure the velocity of targets moving towards or away from the radar (e.g. Convair 580’s along-track interferometric SAR, JPL’sAIRSAR using the L- and C-band channels, RADARSAT-2’s experimental Moving Object Detection (MODEX) - wings of the SAR antenna serve as two independent receivers)


Repeat-pass Satellite Interferometry

• To date, satellite interferometry has been the repeat-pass type, where an image is taken one day, and a second image is taken of the same scene one or more days later (exception SRTM Mission)

• More images can be taken at later intervals and used in the processing, as long as the scene retains reasonable coherence over the longer time interval. Temporal decorrelation times: geologic change takes many years, but only a few seconds of wind produces motion in trees and lake surfaces

• Because there is always a time delay, and usually parallax as well, assumptions must be made, or processing must be done to remove the unwanted component of motion or topography


Principle of Repeat Pass Interferometry

• Based on two image acquisitions of the same scene from slightly displaced orbits of the satellite.

• Phase information of the two image data files is then superimposed.

• The two phase values at each pixel are subtracted, leading to an interferogram that records only the differences in phase between the two original images.

• Phase differences can be related to the altitude variation at each position in the swath and enable the production of a Digital Elevation Model (DEM).


Principle ofRepeat Pass Interferometry

Repeat pass interferometric SAR uses two antenna positions to acquire two SAR images. Vertical height is determined by comparing phase measurements. Observable terrain shifts are on the order of the radar wavelength or smaller.

Pass 1

Pass 2


Geometry of Satellite Repeat-pass InSARS2

R2

B

R1

S1

Earth's surface h

A

S satellite position

R range to point P

B baseline between satellites

A satellite altitude

h height of point P

B⊥

P


InSAR Processing

• Process data to SLC images• Register the two images to 1/10 pixel• Over-sample by a factor of 2 in both dimensions• Filter common bands in spectrum• Conjugate multiply to form interferogram• Smooth the interferogram• Measure coherence • Unwrap the phase• Estimate geometry parameters (especially baseline)• Remove flat-earth fringes• Convert unwrapped phase to height or motion


Limitations of RADARSAT InterferometryCritical issues or requirements• Must use single beam, single look complex (SLC) products• To maintain the coherence there should be no change in

backscatter. (Vegetated sites are a problem and dry conditions are preferable.)

• Results can be affected by anisotropic propagation of one or both of the data takes (mainly variation in atmospheric water vapour content)

• For topographic mapping RADARSAT orbits should be approximately 0.5 - 1.5 km apart

• For detection of feature movement, orbits should as close as possible

• Ground Control Points are required• Knowledge of sensor location is critical; orbit selection is

important


InSAR Applications ~ Topographic Mapping ~

• Conditions for measuring topography -• Satellite InSAR - DEM accuracy• Topographic Mapping Applications

• Cross-track satellite interferometry (SRTM)– Perspective View with Landsat Overlay, Santa Clara River Valley,

California• Repeat-pass satellite interferometry (ERS-1, RADARSAT-1)

– ERS-1 - IHS composite of three image components derived from the interferogram, Schefferville, Québec

– RADARSAT-1 - Image components derived from the interferogram, BathurstIsland, N.W.T

• Cross-track airborne interferometry (Convair-580)– DEM and perspective view of Kananaskis Valley, Alberta

• Cross-track airborne interferometry (Star-3i)– Image and DEM, Baden-Wurttemberg, Germany– Data fusion of topographic map with STAR-3i IFSAR radar image and digital

elevation data, Freiburg, Germany


Topographic Mapping ~ Conditions for Measuring Topography ~

• To measure topography, the following conditions must exist:

• the baseline must lie within acceptable limits• motion in the scene must be negligible• coherence must be high enough (e.g. |γ| > 0.4)

• If the baseline is too small, the sensitivity to topography will be low, and phase noise may dominate

• need B⊥ > 50 m for ERS• If the baseline is too large, phase aliasing may occur

and the coherence will drop • need B⊥ < 300 m for ERS


Satellite InSAR - DEM accuracy

Note: Results from low relief terrain (lowest values) will be better than those from areas with significant relief (highest values).

Satellite Resolution(m)

Accuracy(m)

Notes

ERS –1 andERS-2

24 3-20 For most areas, except tropicalforest or regions with significantvegetation or moisture variability.The ERS-1/2 tandem dataarchive is extensive.

JERS 18 10-20 L-band shows better coherence(for more terrain tyeps and forlonger time periods) than C-band.

RADARSAT(Standardmode)

20-29 10-20 Dry terrain is preferred due to the24-day orbit repeat cycle andpotential loss of coherence.

RADARSAT(Fine mode)

7-9 3-10 Dry terrain preferred. Largerbaselines are possible,increasing accuracy and reducingsensitivity to propagation effects.

Toutin Th., A.L. Gray 2000


The NASA/DLR SRTM Mission

60-m long boom

Auxiliary radar antennas

Main radar antennas

The Space Shuttle


Coverage of 11-day SRTM MissionSRTM Terrain Coverage

East Longitude

LAND OCEAN

NUMBEROF

IMAGINGS

Latit

ude


SRTM Perspective View with Landsat Overlay ~ Santa Clara River Valley, California ~

City of Santa Paula

Pacific Ocean

http://photojournal.jpl.nasa.gov/cgi-bin/PIAGenCatalogPage.pl?PIA02789Image credit: NASA/JPL/NIMA/USGS

Elevation data fromC-band across-trackinterferometric radar,SRTMAcquired Feb. 16, 2000Height exaggeration 2x

Landsat overlayAcquired: Dec. 14, 1984

View toward the North34.42°N 119.17°W


ERS-1 Repeat-Pass Interferometry~ Schefferville, Québec ~

Colour: Interferogram Phase, 16 steps from 0 to 2pi radians Intensity: Interferogram Magnitude Saturation: Coherence

Interferogram Magnitude is the background black-and-white image -similar to regular SAR image.

Coherence (colour brightness) indicates the degree of phase correlation. Low coherence indicates greater change (lakes at upper left). High coherence indicates least change (exposed rocks at lower left).

Colour-coded interferogram phase: a phase change of 2pi radians corresponds to an altitude change of 232 m.Nominal Baseline: 40.1 m Nominal Pixel Size: 20 m (az) x 20 m (ground range)Nominal Scene Size: 20 km by 20 km Nominal Scene Centre: N54.9 W66.6Processor: dtSAR (VMP algorithm)

Pass 1: 15 January 1994 Pass 2: 24 January 1994 9 day separation

http://www.ccrs.nrcan.gc.ca/ccrs/comvnts/rsic/2301/2301rn2e.html


RADARSAT-1 Repeat-Pass Interferometry~ Bathurst Island, N.W.T. ~

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/ana/interfer/interfre.html

Interferogram magnitude Scene coherence

Raw interferogram phase Composite : intensity: interferogram magnitudesaturation: coherencecolour: flat-earth corrected (relative) phase

RADARSAT SAR F5Descending passesNom. incident angle: 47°

Pass 1: 04 March 1996 13:03 UTCPass 2: 28 March 1996 13:03 UTCNom. baseline: 847mHeight of ambiguity: 28 mNominal resolution: 10 m x 12 m (azimuth, ground range)Nominal scene size:10.2 km x 12.3 km(azimuth, ground range)Processor: dtSAROne phase cycle represents a relative change in elevation of 21 m.

Coherence is high (close to 1) for land areas even after the 24 day interval. Areas of reduced coherence are associated with sea ice and the drainage system.Source: Geudtner D. , P.W.Vachon, K. Mattar, A.L. Gray, 1998


DEM derived from RADARSAT Interferometric SAR Data

Western Argentina Coherence image resulting from interferometric processing

Input images: RADARSAT-1 SLC Fine Beam Mode Aug. 24, 1998 & Sept. 17, 1998

Time interval : 24 daysBaseline (B⊥): 1113.7784 m

High quality DEM data is expected where coherence is high (bright)

Geocoded Imageshowing locations of

ground control points

Map scale: 1:250,000Map projection: UTM, WGS84

N

Georeferencing(15 points)

Height calibration (1263 points)


EarthView InSAR Processingfor the Western Argentina DEM

• Process raw SAR data to zero Doppler, slant range projection, phase preserved single-look imagery using Atlantis Scientific’sAdvance Precision Processor (APP).

• Generate interferogram of area of interest. A special slope enhancement algorithm was used which is effective for large baselines and/or steep topography. Fringes in the interferogramcorrespond to approx. 15.975 m of height change.

• Unwrap phase to produce phase image.

• Convert to a height image (heights in slant range projection)

• Remove known radar terrain distortions (for each pixel, distancebetween satellite and ground and the pixel height are known accurately)

• Resample DEM to UTM grid to produce non-calibrated height image. Backscatter and coherence image are also geocoded.


Georeferencing and Height Correctionfor the Western Argentina DEM

• Geocoded DEM and backscatter image were registered to 1:250,000 Argentine topographic map. The supplied ground control points (GCPs), representing oil well sites, were converted to UTM projection, referenced to WGS84.

• For georeferencing, 15 x,y coordinates were used. • Mean error: (x, y, x*x+y*y) = (-0.000, 0.000, 13.030)• RMS error: (9.497, 13.827, 16.774)

• For height calibration, 1263 points were used. The GCPsprovided were concentrated in the centre of the area of interest, not evenly distributed. Primary area of interest was centered on: 37°18' 54"S and 69°13 ’40"W °. Quality control was done on 1203 GCPs (many of these were used in the DEM correction), with an RMS elevation error for 95% of the points between 0 and ±10 m.


Colour-coded DEM Western Argentina

Area of interest (Pampa):36°52’12’’S, 69°37’08’’W 37°45’33’’S, 68°50’23’’W

derived fromRADARSAT-1 Fine

Mode SAR databy Atlantis Scientific

using EarthView InSARsoftware

N


The Convair-580 InSAR System

InSARAntenna Radome

Main AntennaRadome

Real-time Display Station

RF EquipmentRacks

SAR ControlStation

DigitalRecording

Convair 580

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/satsens/sarbro/sbc580e.html


DEM of Kananaskis Valley, Alberta

Source: Laurence Gray and Karim Mattar, CCRShttp://www.ccrs.nrcan.gc.ca/ccrs/tekrd/satsens/sarbro/sbintere.html

Terrain elevations derived from across-track interferometric SAR data

C-band InSAR data – Convair-580 Feb. 1992


Perspective view~ Kananaskis Valley, Alberta ~

Source: Laurence Gray and Karim Mattar, CCRShttp://www.ccrs.nrcan.gc.ca/ccrs/tekrd/satsens/sarbro/sbintere.html

C-band InSAR data – Convair-580 Feb. 1992


The Intermap STAR-3i Aircraft SAR

http://www.intermaptechnologies.com/HTML/mapp_star3i.htm


Baden-Wurttemberg Image and DEM -5 km x 7 km sub-area of Kurnbach ~ 35 km NW of Stuttgart

Image and DEM courtesy of

Technologies Ltd.


STAR-3i SAR Image and DEM

Digital elevation model processed interferometrically

from the STAR-3i raw radar data 2 m vertical accuracy

STAR-3i SAR image Resolution: 2.5 m

Frieburg, Germany

Image and DEM courtesy of

Technologies Ltd.


Map-IFSAR Data Fusion

Data fusion of a standard 1:25,000 German topographic map of the city of Freiburg, with STAR-3i IFSAR radar image and digital elevation data.

Fused images may be used for 3-D views and automated classification. The elevation data for landscape features such as buildings and forests is retained.

Image courtesy of

Technologies Ltd.Image processing and editing by: W.GeileGe matics Consulting


InSAR Applications ~ Measuring Motion of the Earth’s Surface ~

• Applications of differential interferometry

• Measurement of systematic deformation

• Conditions for measuring motion

• Limitations of differential interferometry

• Examples of deformation mapping• Belridge Oil Field, Lost Hills, California• Lost Hills, California• Cold Lake, Alberta

• Multiple scene analysis - the time dimension• Vancouver, B.C.


Applications of Differential Interferometry• Differential SAR interferometry is used to measure:

• vertical height change • horizontal shift in ground range direction • surface scattering change using coherence

measurements

• Applications include: • Subsidence due to oil and gas extraction (Belridge and Lost Hills

areas of California) and groundwater depletion• Deformation due to cyclic steam stimulation for oil recovery (Cold

Lake, Alberta)• Deformation related to geodynamic processes (landslides,

volcanos, earthquakes) • Mapping motion of glaciers, ice streams, ice sheets (examples in

advanced interferometry section)• Classification of land use and change detection using coherence

http://otter.ccrs.nrcan.gc.ca:80/ccrs/tekrd/programs/rudp/rudprepe.html


Differential Interferometry ~ Measurement of Systematic Deformation ~

∆hB - ∆hA = ( ∆φB • ∆φA)λ /4π cosθ

∆hB = (ρ2B - ρ1B)/cosθ

Case A Case B

∆hA = 0

ρ1A = ρ2A

Shift between passes

ρ1Bρ2B

ρ2B - ρ1B = (λ/4π) ∆φB

h = heightρ = slant rangeθ = incident angleφ = phase

Atlantis Scientific Inc.


Satellite InSAR ~ Conditions for Measuring Motion ~

To measure motion, the following must apply:• The time delay must be appropriate to the scale of motion to

be measured (i.e., the motion must obey the Nyquistsampling theorem), and

• The motion must have enough spatial cohesiveness that the coherence is high enough

• Plus one of the three conditions needed to remove the topographic component of the phase:

• the baseline must be small enough that the topography component can be neglected, or

• an accurate DEM must be used to remove the topographic component, or

• three passes must be used to remove the topographic component


Satellite InSAR ~ Limitations of Differential Interferometry ~

• While individual pixel motions may not be that accurate, satellite InSAR has an advantage over in-situmeasurements because it takes a large number of measurements over a wide area. In this way, a velocity field can be constructed, and matched to a geophysical model of the motion (e.g., glaciers and post-seismic deformation).

• Heavy vegetation and damp climates can adversely affect InSAR measurements. Conventional InSARtechniques have failed when applied to landslides resulting from creeping, waterlogged and vegetated slopes. Successful applications are generally in dry areas and areas with stable radar reflections.


Subsidence in the Oil Fields ~ Belridge and Lost Hills Oil Fields, California ~

The Belridge and Lost Hills Oil Fields have been subject to subsidence for the past 10-15 years.

Monitoring of subsidence is being done to understand the relationships between injection, extraction, subsidence and well failures. This knowledge is being used in development of production strategies.

The area around the oil fields has little natural vegetation and recieves little precipitation. Coherence is generally high, and InSARresults correlate well with GPS point measurements of subsidence.

Project in collaboration with Shell Exploration and Production

DEM: ERS tandem ESASAR image: RADARSAT F2 CSA

Deformation map: ERS-1, Sept-Nov 1992



Deformation Mapping~ Ground subsidence due to oil extraction ~

http://www.atlsci.com/library/commercial_apps_of_SAR_interferometry_for_change_detection.htm

Well failures and well failure ratesOil Pressure

Ground subsidence

South Belridge Diatomite WaterfloodWell Failure Rates

Wel

l Fai

lure

Rat

es (%

of a

ctiv

e w

ells

)

Failed Well Pipe

2 miles

1 m

ile

Jan-86

Jan-96Jan-97

Jan-98

Jan-88Jan-89

Jan-87Jan-93

Jan-94Jan-95

Jan-92Jan-91

Jan-90

0%

2%

4%

6%

8%

10%


4 - Pass Differential InSAR~ Belridge and Lost Hills Oil Fields, California ~

1

2

34

5

6

7 8

9

1011

12

1314

15

1 -0.010 m2 -0.058 m3 -0.039 m4 -0.019 m5 -0.021 m6 -0.025 m7 -0.049 m8 -0.026 m9 -0.012 m10 -0.016 m11 -0.020 m12 -0.017 m13 -0.017 m14 -0.017 m15 +0.008 m

© Atlantis Scientific 1997

DEM: ERS 1/2 Tandem + Differential InSAR: ERS-1 92/09/17 and 92/11/26

Height change

http://earth.esa.int/symposia//program-details/data/vanderkooij1/index.html


Lost Hills Oil field, Change in Elevation (subsidence)08/ 20/00 to 01/ 07/ 01 (140 days)

InSAR mapping product

Annual RateInches/Year



InSAR mapping productDecember 28, 1999

DEFORMATION RATE(inches / year)

0.00

-12.50

-27.50



Deformation Mapping~ Deformation due to cyclic steam stimulation

for oil recovery ~ The oil-bearing sands at Cold Lake are buried too deeply for surface mining, so a production process called cyclic steam-stimulation is used to recover the bitumen (heavy oil). Multiple wells are drilled from surface pads. High-temperature steam from a central plant is carried through insulated pipelines and injected at high pressure down the wellbores into the oil-sand formation. The heat sits there for a few weeks, softening the bitumen so it will flow. It is pumped to the surface, processed at a central plant and shipped by pipeline to markets in Canada and the U.S.

Multiple wells are drilled from surface pads. Photo credit: Imperial Oil Ltd http://esso.ca/investors/operating/natural_resources/mn_sands.html#cold_lake

The high pressure of the steam injection causes the surface of the reservoir to heave. After each production cycle (injection, steam soak, pump), the surface subsides.

Imperial Oil produces an average of 47 million barrels of crude oil annually from the Cold Lake project.


RADARSAT Differential Interferogram

Cold Lake Oil FieldMeasurement of deformation due to Cyclic Steam Stimulation process used for recovery of bitumen

RADARSAT F1 ascendingLocation: 54.63372 N, -110.47909 W

Master: orbit 25289, Sept 8, 2000

Slave: orbit 25632, Oct 2, 2000

Perpendicular Baseline: -276 m

Ambiguity Height: -61 m/cycle Atlantis Scientific Inc.

Stancliffe R.P.W. and M. van der Kooij, 2001 http://www.atlsci.com/news.html


Cold Lake RADARSAT Deformation Map September 8 - October 2, 2000

Deformation (m)

Stancliffe S. and M. van der Kooij, 2001 http://www.atlsci.com/news.html


Multiple Scene Analysis ~ Analysis of Permanent Scatterers over Time ~

Differential measurements (~1 mm / yr) for a network of permanent point scattererscan be used to monitor slow deformation due to seismic motion, subway construction, oil production and water pumping. The technique has been demonstrated for rocky terrain and urban areas using ERS SAR data, and is being tested for other types of terrain.

This new InSAR technique relies on the presence of permanent or persistent point scatterers within an area of interest. They are identified from a temporal series of coregistered interferograms.

By constructing a time history of many measurements, atmospheric effects can be filtered out and deformation rates can be determined at the permanent scattererlocations.

StackedInterferograms

Point Target

Phas

e

Point Target Phase Variation with Time


Multiple Scene Analysis~ Analysis of Permanent Scatterers over Time ~

Processing Steps• Coregistration of a large number of SAR scenes (n > ~15)

• Generation of n-1 interferograms

• Relative calibration of interferograms using a stable reference point

• Calculation of “temporal coherence”

• Detection of permanently coherent targets (e.g. buildings)

• Atmospheric filtering

• Measurement of temporal deformation rates (or correlation with other parameters)

• Continuous monitoring of targets

Source: Ferretti, A. C. Prati, R. Focca, 2001


Multiple Scene Analysis~ Analysis of Permanent Scatterers over Time ~

Deformation Rate Map

Displacements at thousands of locations in an area near the Vancouver airport (~4x4 miles) wereplotted. Only 17 targets were detected with an average subsidence of 3.6 mm/yr (0.15"/yr) or more during the period 1992-2000. No targets were detected with significant uplift.

Vancouver No. 5

Dis

plac

emen

t (cm

)

Time (year)

Dis

plac

emen

t (cm

)

Time (year)

Number of Points

Vancouver No. 2 Displacement rate-3.6 mm/year

Displacement rate-0.6 mm/year

Displacement rate[mm/year]

20

01 A

tlant

is S

cien

tific

Inc.

http://orbit35i.nesdis.noaa.gov/orad/sarconference/presentations.html

Radar Agriculture/Hydrology References - Références radar en agriculture et hydrologie

Page 1 of 13Bibliography - Agriculture/Hydrology References

Ahern F. "Forestland Management – Comparing RADARSAT Beam Modes" http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/alb/ralb01f_e.html

Ahern F.J., D.G. Goodenough, A.L. Grey, R.A. Ryerson, and R.J. Vilbikaitis (1978) "SimultaneousMicrowave and Optical Wavelength Observations of Agricultural Targets", Canadian Journal ofRemote Sensing, Vol. 4, No. 2, pp. 127-142

Ahmed S., H.R. Warren, M.D. Symonds, and R.P. Cox (1990) "The Radarsat System", IEEETransactions on Geoscience and Remote Sensing, Vol. 28, pp. 598-602 Ahmed S., R.B. Gray, H.R. Warren, and D.G. Fearn (1989) "The new RADARSAT: An all weathermulti-purpose earth observation spacecraft", Space Technology, Vol. 9, pp. 267-279 Attema E.P. and F.T. Ulaby (1978) "Vegetation modeled as a Water Cloud", Radio Science, Vol. 13,pp. 357-364 Bakhtiari S. and R. Zoughi (1991) "A Model for Backscattering Characteristics of Tall Prairie GrassCanopies at Microwave Frequencies", Remote Sensing of Environment, Vol. 36, pp. 137-147 Barber D.G. , K.P. Hochheim, R. Dixon, D.R. Mosscrop, and M.J. McMullan (1996). "The Role ofEarth Observation Technologies in Flood Mapping; A Manitoba Case Study". Research Note,Canadian Journal of Remote Sensing, Vol. 22, No. 1, pp. 137-143 Beaudoin A., T. Le Toan, and Q.H.J. Gwyn (1990) "SAR Observations and Modeling of the C-BandBackscatter Variability Due to Multiscale Geometry and Soil Moisture", IEEE Transactions onGeoscience and Remote Sensing, Vol. GE-28, pp. 886-895 Bernier M., J.P. Dedieu et J.P. Fortin,(1996) "Suivi du manteau neigeux par radar dans les Alpesfrançaises; Application d'une approche développée au Québec". Journal Canadien de télédétection,Vol. 22, No. 1, March 1996 Bernier M., Y. Gauthier et J.P. Dedieu (été 1995) "Interprétation d’une image radar du satelliteERS-1 prise en période de fonte au Québec: Illustration du phénomène de diffusion dans leshyperfréquences". Revue Photo-Interprétation, Éditions Espa, FRANCE Bernier M., J.P. Fortin et Y. Gauthier (1994) "Suivi du convert nival par le satellite ERS-1: Résultatspréliminaires obtenus dans l'est du Québec". Journal canadien de télédétection, Vol. 20, No. 2, pp.138-149 Bernier M., J.P. Fortin et A. Pesant (1992) "Utilisation de boisés de conifères pour étalonner desdonnées radar (RAS)". Journal canadien de télédétection, Vol. 18, No. 2, pp. 73-88 (CCRS #:1088491) Bernier M. et J.P. Fortin (1991) "Suivi du couvert nival par radar: résultats obtenus dans le Sud duQuébec", Comptes rendus du 7e Congrès de l'Association québécoise de télédétection, octobre1991, Montréal, Canada, pp. 83-92

http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/alb/ralb01f_e.html

Bertuzzi P., A. Chanzy, D. Vidal-Madjar, and M. Autret (1992) "The Use of a Microwave BackscatterModel for Retrieving Soil Moisture over Bare Soil", International Journal of Remote Sensing, Vol.13, pp. 2653-2668 Boisvert J.B., T.J. Pultz, R.J. Brown, and B. Brisco (1996-a) "Potential of Synthetic Aperture Radarfor Large Scale Soil Moisture Monitoring", Canadian Journal of Remote Sensing, Vol. 22, No. 1, pp.2-13 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1488 Boisvert J.B., Y. Crevier, and T.J. Pultz (1996-b) “Estimation régionale de l’humidité du sol partélédétection”, Canadian Journal of Soil Science, Vol. 76, pp. 325-334 Boisvert J.B., Q.H.J. Gwyn, B. Brisco, D.J. Major, and R.J. Brown (1995) “Evaluation of SoilMoisture Estimation Techniques and Microwave Penetration Depth for Radar Applications”,Canadian Journal of Remote Sensing, Vol. 21, pp.110-123 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1238 Boivin F., Q.H.J. Gwyn et K.P.B. Thomson (1990). " Effets de la géométrie de surface de champs demaïs sur la rétrodiffusion du radar bande C", Journal canadien de télédétection, Vol. 16, No. 3, pp.16-28 Bouman B.A.M. and D. Hoekman (1993) "Multi-temporal, Multi-frequency Radar Measurements ofAgricultural Crops During the Agriscatt-88 Campaign in The Netherlands", International Journal ofRemote Sensing, Vol. 14, pp. 1595-1614 Bouman B.A.M. and D. Uenk (1992) "Crop Classification Possibilities with Radar in ERS-1 and JERS-1 Configuration", Remote Sensing of Environment, Vol. 40, pp. 1-13 Bouman B.A.M. (1991-a). "Crop Parameter Estimation from Ground-Based X-Band (3-cm Wave)Radar Backscattering Data", Remote Sensing of the Environment, Vol. 37, pp. 193-205 Bouman B.A.M. (1991-b), "The Linking of Crop Growth Models and Multi-Sensor Remote SensingData", Proceedings of the Fifth International Colloquium on Physical Measurements and Signaturesin Remote Sensing, Courchevel, France, 14-18 January 1991 Bouman B.A.M and H.W.J. van Kasteren (1990-a) "Ground-Based X-Band (3-cm Wave) RadarBackscattering of Agricultural Crops. I. Sugar Beet and Potato; Backscattering and Crop Growth", Remote Sensing of the Environment, Vol. 34, pp. 93-105 Bouman B.A.M. and H.W.J. van Kasteren (1990-b) "Ground-Based X-Band (3-cm Wave) RadarBackscattering of Agricultural Crops. II. Wheat, Barley, and Oats; the Impact of Crop Structure",Remote Sensing of the Environment, Vol. 34, pp. 107-118 Brakke T.W., E.T. Kanemasu, J.L. Steiner, F.T. Ulaby, and E. Wilson (1981) "Microwave Responseto Canopy Moisture, Leaf-Area Index, and Dry Weight of Wheat, Corn, and Sorghum," RemoteSensing of Environment, Vol. 11, pp. 207-220 Brisco B. and S. Ross, “Rice Crop Monitoring” http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/chn/rchn01_e.html Brisco B. and R.J. Brown (1998) “Agricultural Applications with Radar”, Chapter 7, Principles andApplications of Imaging Radar, Manual of Remote Sensing, 3rd edition, Vol. 2; Edited by F.M.Henderson and A.J. Lewis, John Wiley & Sons, Inc., Toronto


http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1488


http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/chn/rchn01_e.html

Brisco B., R.J. Brown, G. Stapes, and D. Nazarenko (1995-a) “Potential Rice Identification andMonitoring with RADARSAT”, 17th Canadian Symposium on Remote Sensing, Proceedings,Saskatoon, Saskatchewan, June 13-15, 1995, pp. 474-479 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1939 Brisco B. and R.J. Brown (1995-b) "Multi-date SAR/VIR Synergism for Crop Classification inWestern Canada", Photogrammetric Engineering and Remote Sensing, Vol. 61. No. 8, pp. 1009-1014 Brisco B., R.J. Brown, J.G. Gairns, and B. Snider (1992), "Temporal Ground-Based ScatterometerObservations of Crops in Western Canada", Canadian Journal of Remote Sensing, Vol. 18. No. 1,pp. 14-22 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1877 Brisco B., T.J. Pultz, R.J. Brown, G.C.Topp, and W.D. Zebchuk (1991-a) "Dielectric ConstantMeasurements of Soil with Portable Dielectric Probes and TDR Techniques", Journal of WaterResources Research, Vol. 28, No. 5, pp. 1339-1346 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/1489.pdf Brisco B., R.J. Brown, B. Snider, G.J. Sofko, J.A. Koehler, and A.G. Wacker (1991-b) "Tillage Effectson the Radar Backscattering Coefficient of Grain Stubble Fields", International Journal of RemoteSensing, Vol. 12, No. 11, pp. 2283-2298 Brisco B., R.J. Brown, J.A. Koehler, G.J. Sofko, and M.J. McKibben (1990-a) "The Diurnal Pattern ofWheat Radar Backscatter", Remote Sensing of Environment, Vol. 34, pp. 37-47 Brisco B. and R.J. Brown (1990-b) "Drought Stress Evaluation in Agricultural Crops Using C-HHSAR Data", Canadian Journal of Remote Sensing, Vol. 16, No. 3, pp. 39-47 Brisco B., R.J. Brown, and M.J. Manore (1989) "Early Season Crop Discrimination with CombinedSAR and TM Data", Canadian Journal of Remote Sensing, Vol. 15, pp. 44-54 Brisco B., F.T. Ulaby, and R. Protz (1984) "Improving crop classifications through attention to thetiming of airborne radar acquisitions", Photogrammetric Engineering and Remote Sensing, Vol. 50,pp. 739-745 Brisco B. and R. Protz (1980) "Corn field identification accuracy using airborne radar imagery",Canadian Journal of Remote Sensing, Vol. 6, No. 1, pp. 15-24 Brown R.J., B. Brisco, R. Leconte, D.J. Major, J.A. Fischer, G. Reichert, K.D. Dorporal, P.R. Bullock,H. Pokrant, and J. Culley (1993-a) “Potential Applications of RADARSAT Data to Agriculture andHydrology”, Canadian Journal of Remote Sensing, Vol. 19, pp. 317-329 (CCRS #: 1099536) http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1164 Brown R.J., B. Brisco, F.J. Ahern, C. Bjerkelund, M. Manore, T.J. Pultz, and V. Singhroy (1993-b)"SAR Application Calibration Requirements", Canadian Journal of Remote Sensing, Vol. 19, No. 3,pp.193-203 (CCRS #: 1099973) http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1160 Brown R.J., M.J. Manore, and S. Poirier (1992) "Correlations Between X, C, and L band ImageryWithin an Agricultural Environment", International Journal of Remote Sensing, Vol. 13, No. 9, pp.1645-1661 (CCRS #: 1087605)




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Brown, R.J., B. Guindon, P.M. Teillet, and D.G. Goodenough (1984) "Crop Type Determiniationfrom Multitemporal SAR Imagery", Ninth Canadian Sym. on Remote Sensing, Proc., St. John's,Canada, 14-17 August 1984, pp. 683-691 (CCRS #:1050069) Bush T.F. and F.T. Ulaby (1978) "An evaluation of radar as a crop classifier", Remote Sensing ofEnvironment, Vol. 7, pp. 15-36 Castro Ríos R. y M. Espinosa Toro (1999) “Análisis de cambio interanual en bosques nativosaustrales con imágenes RADARSAT”, Simposio Final GlobeSAR 2, Buenos Aries, Argentina, 17-20de Mayo 1999, pp. 77-83 http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/chil/chilim01_e.html Champion I. and R. Faivre (1997) “Sensitivity of the Radar Signal to Soil Moisture: Variation withIncidence Angle, Frequency, and Polarization”, IEEE Transactions on Geoscience and RemoteSensing, Vol. 35, pp. 781-783 Chanzy A. (1993) "Basic Soil Surface Characteristics Derived from Active Microwave RemoteSensing", Remote Sensing Reviews, Vol 7, pp. 303-319 Cihlar J., T.J. Pultz, and A.L. Gray (1992) "Change Detection with Synthetic Aperture Radar",International Journal of Remote Sensing, Vol. 13, No. 3, pp. 401-414 Cihlar J., M.C. Dobson, T. Schmugge, P. Hoogeboom, A.R.P. Janse, F. Baret, G. Guyot, T. Le Toan,and P. Pampaloni (1987) "Procedures for the Description of Agricultural Crops and Soils in Opticaland Microwave Remote Sensing Studies", International Journal of Remote Sensing, Vol. 8, pp. 427-439 Costa M.P.F., E.M.M. Novo, F. Mitsuo, J.E. Matovani, R.V.Ballester, and F. Ahern (1998) “SeasonalDynamics of the Amazon Floodplain through RADAR eyes: Lago Grande de Monte Alegre CaseStudy”, RADARSAT for Amazonia: Results of ProRADAR Investigations, CCRS publication, pp. 163-171 Cotlier C.G., A. Ravenna y M.F. Huisman (1999) “Relación de la retrodipsersión en imágenesurbanas RADARSAT Fine Beam 1 (Cuidad de Rosario, Argentina) con relación a la pobreza urbana,la densidad poblacional urbana obtenida de datos censales y programa CEI para mejoamiento deimágenes“, Simposio Final GlobeSAR 2, Buenos Aries, Argentina, 17-20 de Mayo 1999, pp. 415-420 http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/arg/argim29_e.htm Crevier Y. and T.J. Pultz (1996-a) “Flood Monitoring Using Multi-Date, Multi-Incidence Angle C-band SAR Data”, Third International Workshop on Applications of Remote Sensing in Hydrology,Greenbelt, Maryland (USA), 16-18 October 1996 Crevier Y. and T.J. Pultz (1996-b) “Analysis of C-band SIR-C/X Sar Radar Backscatter Over aFlooded Environment, Red River, Manitoba”, 3rd International Symposium on Applications ofRemote Sensing in Hydrology, Greenbelt, Maryland (USA), 16-18 October 1996, pp. 47-60 Crevier Y., T.J. Pultz, T.I. Lukowski, and T. Toutin (1996-c) "Temporal Analysis of ERS-1 SARBackscatter for Hydrology Applications", Canadian Journal of Remote Sensing, Vol. 22, No. 1, pp.65-76 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1490 Daughtry C.S.T., K.J. Ranson, and L.L. Biehl (1991) "C-band Backscattering from Corn Canopies",



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International Journal of Remote Sensing, Vol. 12, No. 5, pp. 1097-1109 Dobson M.C., L. Pierce, K. Sarabandi, F.T. Ulaby, and T. Sharik (1992) "Preliminary Analysis ofERS-1 for Forest Ecosystem Studies", Transactions on Geoscience and Remote Sensing, Vol. 30,pp. 203-211. Dobson M.C. and F.T. Ulaby (1986) " Active Microwave Soil Moisture Research", IEEE Transactionson Geoscience and Remote Sensing, Vol. GE-24, pp 23-36 Dobson M.C., F. Kouyate, and F.T. Ulaby (1984) "A Re-examination of Soil Textural Effects onMicrowave Emission and Backscattering", IEEE Transactions on Geoscience and Remote Sensing,Vol. GE-22, pp. 530-535 Dobson M.C. and F.T. Ulaby (1981) "Microwave Backscatter Dependence on Surface Roughness,Soil Moisture, and Soil Texture: Part III-Soil Tension", IEEE Transactions on Geoscience andRemote Sensing, Vol. GE-19, pp. 51-61 Donald J.R., F.R. Seglenicks, E.D. Soulis, N. Kouwen, and D.W. Mullins (1993) "Mapping PartialSnowcover During the Melt Season Using C-Band SAR Imagery", Canadian Journal of remoteSensing, Vol. 19, No. 1, pp. 68-76 Dubois P.C., J. van Zyl, and T. Engman (1995) “Measuring Soil Moisture with Imaging Radars”,IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-33, pp. 915-926 El-Rayes M.A. and F.T. Ulaby (1987) "Microwave Dielectric Spectrum of Vegetation--Part I:Experimental Observations", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-25,No. 5, pp. 541-549 Engman E.T. (1991) "Applications of Microwave Remote Sensing of Soil Moisture for WaterResources and Agriculture", Remote Sensing of the Environment, Vol. 35, pp. 213-226 Engman E.T. (1990) "Progress in Microwave Remote Sensing of Soil Moisture", Canadian Journal ofRemote Sensing, Vol. 16, pp. 6-14 Engman E.T. and J.R. Wang (1987) "Evaluating Roughness Models of Radar Backscatter", IEEETransactions on Geoscience and Remote Sensing, Vol. GE-25, pp. 709-713 Epiphanio J.C.N., M.S. Simões y A.R. Formaggio, and C.C. Freitas (1999) “Monitoring Agriculturewith RADARSAT Data”, Simposio Final GlobeSAR 2, Buenos Aries, Argentina, 17-20 de Mayo 1999, pp. 2-7 http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/bra/braim22_e.html Evans D.L., T.G. Farr, and J.J. van Zyl (1992) "Estimates of Surface Roughness Derived fromSynthetic Aperture Radar (SAR) Data", IEEE Transactions on Geoscience and Remote Sensing, Vol.GE-30, pp. 382-389>/div> Ferrazzoli P., S. Paloscia, P. Pampaloni, G. Schiavon, D. Solimini, and P. Coppo (1992) "Sensitivityof Microwave Measurements to Vegetation Biomass and Soil Moisture Content: A Case Study", IEEETransactions on Geoscience and Remote Sensing, Vol. GE-30, pp. 750-756 Foody G.M., M.B. McCulloch, and W.B. Yates (1994) "Crop Classification from C-Band PolarimetricRadar Data", International Journal of Remote Sensing, Vol. 15, pp. 2871-2885 Foody G.M. (1991) "Soil Moisture Content Ground Data for Remote Sensing Investigations of


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Agricultural Regions", International Journal of Remote Sensing, Vol. 12, pp. 1461-1469 Foody G.M., P.J. Curran, G.B. Groom, and D.C. Munro (1989) "Multi-temporal airborne syntheticaperture radar data for crop classification", Geocarto International, Vol. 3, pp. 19-29 Freeman A., J. Villasenor, J.D. Klein, P. Hoogeboom, and J. Groot (1994) "On the Use of Multi-frequency and Polarimetric Radar Backscatter Features for Classification of Agricultural Crops",International Journal of Remote Sensing, Vol. 15, pp. 1799-1812 Fung A.K. and K.S. Chen (1992) "Dependence of the Surface Backscattering Coefficients onRoughness, Frequency and Polarization States", International Journal of Remote Sensing, Vol. 13,pp 1663-1680 Gillespie T.J., B. Brisco, R.J. Brown, and G.J. Sofko (1990) "Radar Detection of a Dew Event inWheat", Remote Sensing of Environment, Vol. 33, pp. 151-156 (CCRS #: 1078841) Gogineni S., J. Ampe, and A. Budihardjo (1991) "Radar Estimates of Soil Moisture Over KonzaPrairie", International Journal of Remote Sensing, Vol. 12, No. 11, pp. 2425-2432 Haralick R.M. (1979) "Statistical and Structural Approaches to Texture", Proceedings of the IEEE,Vol. 67, No. 5, pp. 786-804 Haralick R.M., K. Shanmugan, and I. Dinstein (1970) "Using Radar Imagery for CropDiscrimination-A Statistical and Conditional Probability Study", Remote Sensing of Environment,Vol. 1, pp. 131-142 Hirosawa H., S. Komiyama, and Y. Matsuzaka (1978) "Cross-polarized radar backscatter frommoist soil", Remote Sensing of Environment, Vol. 7, pp. 211-217 Hoekman D.H. and B.A.M. Bouman (1993) "Interpretation of C-and X-band Radar Images Over anAgricultural Area, the Flevoland Test Site in the Agriscatt-87 Campaign", International Journal ofRemote Sensing, Vol. 14, pp. 1577-1594 Holmes M.G. (1990) "Applications of Radar in Agriculture", Chapter 19 in Applications of RemoteSensing in Agriculture, pp. 307-330, Butterworth Publishing Co., Stoneham, MA, 02180 Hoogeboom P.(1983) "Classification of agricultural crops in radar images", IEEE Transactions onGeoscience and Remote Sensing, Vol. GE-21, pp. 329-336 Huerta Sánchez P., V. Barrena Arroyo y C. Garnica Philipps (1999) “Cambios producidos por elFenómeno del Niño en el ecosistema manglares de Tumbes – Perú, detectados por imagénesRADARSAT”, Simposio Final GlobeSAR 2, Buenos Aries, Argentina, 17-20 de Mayo 1999, pp. 318-325 Hutton C.A. and R.J. Brown (1989) “Effect of Row Aspect, and Incidence Angle on RadarBackscatter”, International Geoscience and Remote Sensing Symposium - IGARSS '89/12thCanadian Symposium on Remote Sensing, Proceedings, Vancouver, Canada, 10-14 July 1989, pp.1156-1159 (CCRS #: 1072316) Jackson T.J., H. McNairn, M.A. Weltz, B. Brisco, and R.J. Brown (1997) “First Order SurfaceRoughness Correction of Active Microwave Observations for Estimating Soil Moisture”, IEEETransactions on Geoscience and Remote Sensing, Vol. 35, pp. 1065-1069 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2267



Jobin D.I. and T.J. Pultz (1996) "Assessment of three distributed hydrological models for use withremotely sensed inputs", Third International Workshop on Applications of Remote Sensing inHydrology, Greenbelt, Maryland (USA), 16-18 October 1996, pp. 109-130 Krohn M.D., N.M. Milton, and D.B. Segal (1983) "SEASAT Synthetic Aperture Radar (SAR)Response to Lowland Vegetation Types in Eastern Maryland and Virginia", Journal of GeophysicalResearch, Vol. 88, No. C3, pp. 1937-1952 Kurosu T., M., Fujita, and K. Chiba (1995) “Monitoring of Rice Crop Growth from Space Using theERS-1 C-band SAR”, IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-33, pp. 1092-1096 Kux H.J.H. , J. R. dos Santos, F. Ahern, R. W. Pietsch, and M. S. Lacruz (1998) “Evaluation ofRADARSAT for Land Use and Land Cover Dynamics in the Southwestern Brazilian Amazon State ofAcre”, Canadian Journal of Remote Sensing, Vol. 24 , No. 4, pp. 350-359 Leconte R. and P.D. Klassen (1991-a) "Lake and River Ice Investigations in Northern Manitobausing Airborne SAR Imagery". Arctic, Vol. 44, Supp. 1, pp. 153-163 Leconte R. and T.J. Pultz (1991) "Evaluation of The Potential of RADARSAT for Flood Mapping UsingSimulated Satellite Imagery", Canadian Journal of Remote Sensing, Vol. 17, No. 3, pp. 241-249(CCRS #: 1082471) Leconte R. and T.J. Pultz (1989) “Soil Moisture Information From SAR Images: Estimation of theEffect of Soil Surface Roughness”, International Geoscience and Remote Sensing Symposium -IGARSS '89/12th Canadian Symposium on Remote Sensing, Proceedings, Vancouver, Canada, 10-14 July 1989, pp. 2752-2754 (CCRS #: 1072702) Lemoine G.G., G.F. de Grandi, and A.J. Sieber (1994) "Polarimetric Contrast Classification ofAgricultural Fields Using MAESTRO 1 AIRSAR Data", International Journal of Remote Sensing, Vol.15, pp. 2851-2869 Le Toan T., F. Ribbes, L. Wnag, N. Floury, K. Ding, J. Kong, M. Fujita, and T. Kurosu (1997) “RiceCrop Mapping and Monitoring Using ERS-1 Data Based on Experiment and Modeling Results”, IEEETransactions on Geoscience and Remote Sensing, Vol. GE-35, pp. 41-56 Le Toan T., H. Laur, E. Mougin, and A. Lopes (1989) "Multitemporal and Dual-PolarizationObservations of Agricultural Vegetation Covers by X-band SAR Images", IEEE Transactions onGeoscience and Remote Sensing, Vol. 27, No. 6, pp. 709-718 Liu H.L. and A.K. Fung (1988) "An Empirical Model for Polarized and Cross-Polarized Scatteringfrom a Vegetation Layer", Remote Sensing of the Environment, Vol. 25, pp. 23-36 Major D.J., F.J. Larney, B. Brisco, C.W. Lindwall, and R.J. Brown (1993) "Tillage Effects on RadarBackscatter in Southern Alberta", Canadian Journal of Remote Sensing, Vol. 19, No. 2, pp. 170-178 Martin R.D., G. Asrar, and E.T. Kanemasu (1989) "C-Band Scatterometer Measurements of aTallgrass Prairie", Remote Sensing of Environment, Vol. 29, pp. 281-292 Wankiewicz M.A. (1996) “Rocky Moutain Snowmelt on Tallus Slopes by Radar Satellite”. CanadianJournal of Remote Sensing, Vol. 22, No. 1, pp. 77-94 McNairn H., “Agricultural Remote Sensing Research in Preparation for RADARSAT-2” http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/agri/rsat2/crop_e.html


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McNairn H., C. Duguay, J. Boisvert, E. Huffman, and B. Brisco (2001) “Defining the Sensitivity ofMulti-frequency and Multi-polarized Radar Backscatter to Post-Harvest Crop Residue”, CanadianJournal of Remote Sensing, in press. McNairn H., J.J. van der Sanden, R.J. Brown, and J. Ellis (2000) “The Potential of RADARSAT-2 forCrop Mapping and Assessing Crop Condition”, Second International Conference on GeospatialInformation in Agriculture and Forestry, Lake Buena Vista, Florida (USA), 10-12 January 2000, Vol.2, pp. 81-88 http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/agri/crop_id/overview_e.html McNairn, H., R.J. Brown, J. Ellis, and D. Wood (1998-a) “Extraction of Crop Information fromRADARSAT-1 Imagery”, ADRO Final Symposium, Montreal, Canada, 12-15 october 1998 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3594.pdf McNairn H., D. Wood, Q.H.J. Gwyn, R.J. Brown, and F. Charbonneau (1998-b) “Mapping Tillage andCrop Residue Management Practices with RADARSAT”, Canadian Journal of Remote Sensing, Vol.24, pp. 28-35 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3321.pdf McNairn H., J.B. Boisvert, C. Duguay, E. Huffman, and R.J. Brown (1997) “Investigating theRelationship Between Crop Residue Cover and Radar Backscatter”, International Symposium,Geomatics in the Era of RADARSAT (GER'97), Ottawa, Canada, 25-30 May 1997 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/2278.pdf McNairn, H, J.B. Boisvert, D. Major, Q.H.J. Gwyn, R.J. Brown, and A. Smith (1996) “Identificationof Agricultural Tillage Practices from C-Band Radar Backscatter”, Canadian Journal of RemoteSensing, Vol. 22, No. 2, pp. 154-162 McNairn H.E. and R. Protz (1993) "Mapping Corn Residue Cover on Agricultural Fields in OxfordCounty using TM", Canadian Journal of Remote Sensing, Vol. 19, No. 2, pp. 152-159 Michelson D.B. (1994) "ERS-1 SAR Backscattering Coefficients From Bare Fields with DifferentTillage Row Direction", International Journal of Remote Sensing, Vol. 15, pp. 2679-2685 Moran M.S., A. Vidal, D. Troufleau, J. Qi, T.R. Clarke, P.J. Printer, Jr., T.A. Mitchell, Y. Inoue, andC.M.U. Neale (1997) “Combining Multifrequency Microwave and Optical Data for CropManagement”, Remote Sensing of the Environment, Vol. 61, pp. 96-109 Oh Y., K. Sarabandi, and F.T. Ulaby (1992) "An Empirical Model and an Inversion Technique forRadar Scattering from Bare Soil Surfaces", IEEE Transactions on Geoscience and Remote Sensing,Vol. 30, No. 2, pp. 370-381 Ormsby J.P., B.J. Blanchard, and A.J. Blanchard (1985) "Detection of Lowland Flooding UsingActive Microwave Systems", Photogrammetric Engineering and Remote Sensing, Vol. 51, No. 3, pp.317-328 Paris J.F (1990) "On the Uses of Combined Optical and Active-Microwave Image Data forAgricultural Applications", Applications of Remote Sensing in Agriculture, M.D. Steven and J.A.Clark [Eds.]. Butterworths, Toronto Paris J.F. and H.H. Kwong (1988) "Characterization of Vegetation with Combined Thematic Mapper(TM) and Shuttle Imaging Radar (SIR-B) image data", Photogrammetric Engineering and RemoteSensing, Vol. 54, pp. 1187-1193


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Paris J.F (1986) "The Effect of Leaf Size on the Microwave Backscattering by Corn", RemoteSensing of the Environment, Vol. 19, pp. 81-95 Paris J.F. (1983) "Radar Backscattering Properties of Corn and Soybeans at Frequencies of 1.6,4.75, and 13.3 GHz", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-21, pp. 392-400 Pedroso E.C., B. Rivard, A.P. Crósta, C.R. de Souza Filho, and F.P. de Miranda (2001)"Reconnaissance Geologic Mapping in the Tapajós Mineral Province, Brazilian Amazon, usingSpaceborne SAR Imagery and Airborne Geophysics", accepted for publication by Canadian Journalof Remote Sensing Pietroniro A., E.D. Soulis, N. Kouwen, O. Rotunno, and D.W. Mullins (1993) "Using Wide Swath C-Band SAR Imagery for Basin Soil Moisture Mapping", Special Issue, Canadian Journal of RemoteSensing, January, pp. 77-82 Poirier S., K.P.B. Thomson, A. Condal, and R.J. Brown (1988) "SAR Applications in Agriculture: AComparison of Steep and Shallow Mode (30o and 53o Incidence Angles) Data", InternationalJournal of Remote Sensing, Vol. 10, pp. 1085-1092 Prevot L., I. Champion, and G. Guyot (1993-a) "Estimating Surface Soil Moisture and Leaf AreaIndex of a Wheat Canopy Using a Dual-Frequency (C and X Bands) Scatterometer", RemoteSensing of the Environment, Vol. 46, pp. 331-339 Prevot L., M. Dechambre, O. Taconet, D. Vidal-Madjar, M. Normand, and S. Galle (1993-b)"Estimating the Characteristics of Vegetation Canopies with Airborne Radar Measurements",International Journal of Remote Sensing, Vol. 14, pp. 2803-2818 Pultz T. and J. Sokol, “Honduras Flooding Resulting from Hurricane Mitch” http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/hydro/honduras/mitch_e.html Pultz T.J., Y. Crevier, R.J. Brown, and J. Boisvert (1997-a) “Monitoring of Local EnvironmentalConditions with SIR-C/X-SAR”, Remote Sensing of Environment, Vol. 59, No. 4, pp. 248-255 Pultz T.J., Y. Crevier, B. Brisco, R.J. Brown, and Q.H.J. Gwyn (1997-b) “Soil Moisture Estimationwith RADARSAT”, Proceedings, International Society for Optical Engineering (SPIE), 22-25 Sept.1997, London, UK, pp. 143-148 Pultz T.J. (1997-c) “RADARSAT Tracks Red River Flood”, Remote Sensing in Canada / newsletteredited by: C. Langham, Vol. 25, No. 2, pp.1 http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/man/rman01f_e.html Pultz T.J. and Y. Crevier (1996-a) "Early Demonstration of RADARSAT for Applications inHydrology", Third International Workshop on Applications of Remote Sensing in Hydrology,Greenbelt, Maryland (USA), 16-18 october 1996, pp. 271-282. Pultz T.J. and Y. Crevier (1996-b) “Estimation of Snow Areal Extent Using RADARSAT Data”, 26thRemote Sensing of Environment / 18th Canadian Symposium on Remote Sensing, Vancouver,Canada, 25-29 March 1996, pp. 579 http://www.ccrs.nrcan.gc.ca/ccrs/com/rsnewsltr/2401/2401ap4_e.html Pultz T.J., R. Leconte, L. St.Laurent, and L. Peters (1991) "Flood Mapping with Airborne SARimagery : Case of the 1987 St. John River Flood". Canadian Water Resources Journal, Vol. 16, No.


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2, pp. 173-189 Pultz T.J., R. Leconte, R.J. Brown, and B. Brisco (1990) "Quantitative Estimation of Soil Moisturefrom Airborne SAR Data", Canadian Journal of Remote Sensing, Vol. 16, No. 3, pp. 56-62 Pultz T.J. , R. Leconte, R.J. Brown, B. Brisco, and T. Lukowski (1989) “SAR Response to Spatial andTemporal Variations in Soil Moisture and Vegetation”, International Geoscience and RemoteSensing Symposium - IGARSS '89/12th Canadian Symposium on Remote Sensing, Proceedings,Vancouver, Canada, 10-14 July 1989, pp. 2755-2757 (CCRS #: 1072703) Pultz T. and R.J. Brown (1987) "SAR Image Classification of Agricultural Targets Using First- andSecond-Order Statistics", Canadian Journal of Remote Sensing, Vol. 13, pp. 85-91 Randall D.S. (1994) “Crop Condition Assessment using C- and L-band Polarimetric Radar Data forAltona, Manitoba 1993”, Senior Honours Thesis, Bach. Env. Studies, Dept. of Geog., Fac. OfEnvironmental Studies, Univ. of Waterloo, Canada RESORS (1990). "RDDP agriculture reference list", Canadian Journal of Remote Sensing, Vol. 16,No. 3, pp. 64-66 Rosenthal W.D., B.J. Blanchard, and A.J. Blanchard (1985) "Visible\Infrared\Microwave AgricultureClassification, Biomass, and Plant Height Algorithms", IEEE Transactions on Geoscience andRemote Sensing, Vol. GE-23, No. 2, pp. 84-90 Rosenthal W.D. and B.J. Blanchard (1984) "Active Microwave Responses: An Aid in Improved CropClassification", Photogrammetric Engineering and Remote Sensing, Vol. 50, No. 4, pp. 461-468 Ross S., B. Brisco, R.J. Brown, S. Yun, and G. Staples (1998) “Temporal Signature Analysis of RicePaddies Using RADARSAT-1: Preliminary Results”, 20th Canadian Symposium on Remote Sensing,Calgary, Canada, 11-14 May 1998, pp. 157-160 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3507.pdf Rotunno Filho O.C., E.D. Soulis, A. Abdeh-Kolahchi, N. Kouwen, T.J. Pultz, and Y. Crevier (1996)"Soil Moisture in Pasture Fields Using SAR Data: Preliminary Results", Canadian Journal of RemoteSensing http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1510 Saatchi S.S., D.M. Le Vine, and R.H. Lang (1994) "Microwave Backscattering and Emission Modelfor Grass Canopies", IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, pp. 177-186 Sano E.E., M.S. Moran, A.R. Huete, and T. Miura (1998) “C- and Multiangle Ku-Band SyntheticAperture Radar Data for Bare Soil Moisture Estimation in Agricultural Areas”, Remote Sensing ofthe Environment, Vol. 64, pp. 77-90 Schmullius C. and R. Furrer (1992-a) "Frequency Dependence of Radar Backscattering UnderDifferent Moisture Conditions of Vegetation-Covered Soil", International Journal of Remote Sensing,Vol. 13, pp. 2233-2245 Schmullius C. and R. Furrer (1992-b) "Some Critical Remarks on the Use of C-Band Radar Data forSoil Moisture Detection", International Journal of Remote Sensing, Vol. 13, pp. 3387-3390 Shanmugan K.S., F.T. Ulaby, V. Narayanan, and C. Dobson (1983) "Identification of Corn FieldsUsing Multidate Radar Data", Remote Sensing of Environment, Vol. 13, pp. 251-264




Shao Yun, H. D. Gou, H. Liu, X. Fan, J. Liao et al. (2000) “Chinese SAR for Yangtze River FloodMonitoring in 1998”, Proc. IEEE IGARSS’2000, Honolulu, Hawaii (USA), 24-28 July 2000 Singhroy V. (1996) “Interpretation of SAR images for Coastal Zone Mapping in Guyana”, CanadianJournal of Remote Sensing, Vol. 22 , No. 3, pp. 317-328 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1564 Smith A.M. and D.J.Major (1996) “Radar Backscatter and Crop Residues”, Canadian Journal ofRemote Sensing, Vol. 22, pp. 243-247 Smith A. M., D.J. Major, M.J. Hill, W.D. Willms, B. Brisco, C.W. Lindwall, and R.J. Brown (1993)"Complementarity Of Radar And Visible-infrared Sensors In Assessing Rangeland Condition",Proceedings, 16th Canadian Symposium On Remote Sensing / 8th Congrès de l'Associationquébecoise de télédétection, Sherbrooke, Canada, 7-10 June 1993, pp. 331-336 (CCRS #:1100267) Sofko G.J., A.G. Wacker, J.A. Koehler, M.J. McKibben, R.J. Brown, and B. Brisco (1989). "Groundmicrowave operations", Canadian Journal of Remote Sensing, Vol. 15, No. 1, pp. 14-27 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1875 Stolp J. and A.R.P. Janse (1986) "X-Band Radar Backscattering for Detecting Spatial Distribution ofSoil Slaking", ITC Journal, Vol. 1986-1, pp. 82-87 Taconet O., M. Benallegue, D. Vidal-Madjar, L. Prevot, M. Dechambre, and M. Normand (1994)“Estimation of Soil and Crop Parameters for Wheat from Airborne Radar Backscattering Data in Cand X Bands”, Remote Sensing of the Environment, Vol. 50, pp 287-294 Thomson K.P.B., G. Edwards, R. Landry, A. Jaton, S.P. Cadieux, and Q.H.J. Gwyn (1990) "SARApplications in Agriculture: Multiband Correlation and Segmentation", Canadian Journal of RemoteSensing, Vol. 16, pp. 47-54 (CCRS #: 1078782) Thomson K.P.B., S. Poirier, G.B. Benie, C. Gosselin, and G. Rochon (1989) "Filter Selection andProcessing Methodology for Synthetic Aperture Radar (SAR) Data in Agricultural Applications",Canadian Journal of Remote Sensing, Vol. 13, pp. 6-10 Touré A., K.P.B. Thomson, G. Edwards, R.J. Brown, and B. Brisco (1994) "Adaptation of theMIMICS Backscattering Model to the Agricultural Context: Wheat and Canola at L and C Bands",IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No 1, pp. 47-61 Touré, A., K.P.B. Thomson, G. Edwards, R.J. Brown, and B. Brisco (1991) “Applying the MIMICSBackscattering Model in an Agricultural Context”, Canadian Journal of Remote Sensing, Vol.17, No.4, Oct. 1991, pp. 339-347 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1876 Ulaby F.T., K. Sarabandi, K. McDonald, M. Whitt, and M.C. Dobson (1990) "Michigan microwavecanopy scattering model", International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1223-1253 Ulaby F.T., D. Held, M.C. Dobson, K.C. McDonald, and T.B. Senior (1987-a) "Relating PolarizationPhase Difference of SAR Signals to Scene Properties", IEEE Transactions on Geoscience andRemote Sensing, Vol. GE-25, No. 1, pp. 83-91 Ulaby F.T. and M.A. El-Rayes (1987-b) "Microwave Dielectric Spectrum of Vegetation, Part II: DualDispersion Model", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-25, pp. 550-557





Ulaby F.T., F. Kouyate, B. Brisco, and T.H. Lee Williams (1986-a), "Textural Information in SARImages", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-24, No. 2, March 1986,pp. 235-245 Ulaby, F.T., R.K. Moore, and A.K. Fung (1986-b) Microwave Remote Sensing: Active and Passive,Vol. II and Vol. III, Artech House Inc., Norwood, MA Ulaby F.T., C.T. Allen, G. Eger, and E. Kanemasu (1984) "Relating the Microwave BackscatteringCoefficient to Leaf Area Index", Remote Sensing of Environment, Vol. 14, pp. 113-133 Ulaby F.T., B. Brisco, and M.C. Dobson (1983) "Improved Spatial Mapping of Rainfall Events withSpaceborne SAR Imagery", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-21, No.1, January, pp. 118-121 Ulaby F.T., R.Y. Li, and K.S. Shanmugan (1982). "Crop Classification Using Airborne Radar andLandsat Data", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-20, pp. 518-528 Ulaby F.T., P.P. Batlivala, and J.E. Bare (1980) "Crop identification with L-band radar",Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 1, pp. 101-106 Ulaby F.T. and J.E. Bare (1979) "Look Direction Modulation Function of the Radar BackscatteringCoefficient of Agricultural Fields", Photogrammetric Engineering and Remote Sensing, Vol. 45, No.11, pp. 1495-1506 Ulaby F.T., P.P. Batlivala, and M.C. Dobson (1978) "Microwave Backscatter Dependence on SurfaceRoughness, Soil Moisture, and Soil Texture: Part I-Bare Soil", IEEE Transactions on Geoscience andRemote Sensing, Vol. GE-16, pp. 286-295 Ulaby F.T. and Batlivala, P.P. (1976-a) "Optimum radar parameters for mapping soil moisture",IEEE Trans. on Geoscience and Remote Sensing, Vol. GE-14, pp. 81-93 Ulaby F.T. and P.P. Batlivala (1976-b). "Diurnal Variations of Radar Backscatter from a VegetationCanopy", IEEE Transactions on Antennas and Propagation, Vol. AP-24, No. 1, pp. 11-17 Ulaby F.T. and T.F. Bush (1976-c) "Monitoring Wheat Growth with Radar", PhotogrammetricEngineering and Remote Sensing, Vol. 42, No. 4, pp. 557-568 Ulaby F.T. (1975) "Radar Response to Vegetation", IEEE Transactions on Antennas andPropagation, Vol. AP-23, No. 1, pp. 36-45 Wegmuller U. (1993) "Signature Research for Crop Classification by Active and PassiveMicrowaves", International Journal of Remote Sensing, Vol. 14, pp. 871-883 Wegmuller U. (1990) "The Effect of Freezing and Thawing on the Microwave Signatures of BareSoil", Remote Sensing of the Environment, Vol. 33, pp. 123-135 Wever T. and J. Henkel (1995) “Evaluation of the AIRSAR System for Soil Moisture Analysis”,Remote Sensing of the Environment, Vol. 53, pp. 118-122 Whitt M.W. and F.T. Ulaby (1994) "Radar Response of Periodic Vegetation Canopies", InternationalJournal of Remote Sensing, Vol. 15, pp. 1811-1848 Wood, D., H. McNairn, R.J. Brown, and R. Dixon (2001) "Using RADARSAT-1 for Crop Monitoring:Choosing Between Ascending and Descending Orbits", Submitted to Remote Sensing of the


Environment. http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/13055.pdf Wood, D., R.J. Brown, and H. McNairn (1998) ”Operational Considerations in Using RADARSATfor Agricultural Monitoring”, 20th Canadian Sym. on Remote Sensing, Calgary, Canada, 11-14 May1998 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=3474 Wu L.K., R.K. Moore, and R. Zoughi (1985) "Sources of Scattering from Vegetation Canopies at 10Ghz", IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-23, No. 5, pp. 737-745 Yanasse C.C.F., S. Quegan, and R.J. Martin (1992) "Inferences on Spatial and Temporal Variabilityof the Backscatter from Growing Crops Using AgriSAR Data, Remote Sensing of the Environment,Vol. 13, pp. 493-507 Zoughi R., J. Bredow, and R.K. Moore (1987) "Evaluation and Comparison of DominantBackscattering Sources at 10 Ghz in Two Treatments of Tall-Grass Prairie", Remote Sensing ofEnvironment, Vol. 22, pp. 395-412.




Radar Forestry References - Références radar en foresterie

Ahern F., R. Landry, I. McKirdy, V. Janusauskas, A. Banner, J. Russell, and T. Balce (1997)“Factors Affecting Clearcut Mapping Accuracy from Single-Date RADARSAT Images”, InternationalSymposium, Geomatics in the Era of RADARSAT (GER'97), Ottawa, Canada, 25-30 May 1997 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2051 Chauhan N.S., R.H. Lang, and K.J. Ranson (1991) "Radar modeling of a boreal forest", IEEETransactions on Geoscience and Remote Sensing, Vol. 29, No. 4, pp. 627-638 (CCRS #: 1082073) Costa, M.P.F., E.M.M. Novo, F. Mitsuo, J.E. Matovani, R.V. Ballester, and F. Ahern (1998) “SeasonalDynamics of the Amazon Floodplain through RADAR eyes: Lago Grande de Monte Alegre CaseStudy”, RADARSAT for Amazonia: Results of ProRADAR Investigations, CCRS publication, pp. 163-171 Drieman J.A., F.J. Ahern, and I.G.W. Corns (1989) "Visual interpretation results of multipolarizationC-SAR imagery of Alberta boreal forest", Proceedings IGARSS '89, Vancouver, Canada, Vol. 3, pp.1401-1405 (CCRS #: 1072374) Dobson, M.C., F.T. Ulaby, T. LeToan, A. Beaudoin, E.S. Kasischke, and N. Christensen (1992)“Dependence of Radar Backscatter on Coniferous Forest Biomass”, IEEE Trans. on Geoscience andRemote Sensing, Vol. 30, No. 2, March 1992, pp. 412-415 dos Santos J.R. , H.J.H. Kux, M.S. Lacruz, F. Ahern, and R. Pietsch (1998) “Dynamics of RADARSATBackscattering Values Related to Primary and Secondary Forest Biomass Structure in SWAmazonia, Brazil”, ISPRS Commission VII Symposium, Budapest, Hungary, 1-4 September 1998,pp. 527-531 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=3588 Durden S.L., J.D. Klein, and H.A. Zebker (1991) "Polarimetric radar measurements of a forestedarea near Mt. Shasta", IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 3, pp.444-450 (CCRS #: 1080720) Durden S.L., J.J. van Zyl, and H.A. Zebker (1988) "Modeling and observation of the radarpolarization signature of forested areas", IEEE Transactions on Geoscience and Remote Sensing,Vol. 27, No. 3, pp. 290-301 (CCRS #: 1071154) Evans D.L., T.G. Farr, J.J. van Zyl, and H.A. Zebker (1988) "Radar polarimetry: Analysis tools andapplications", IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 6, pp. 774-789(CCRS #: 1066978) Hess L.A., J.M. Melack, and D.S. Simonett (1990) "Radar detection of flooding beneath the forestcanopy: A review", International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1313-1325 (CCRS#: 1076725) Kux H.J.H., J.R. dos Santos, F. Ahern, R.W. Pietsch, and M.S. Lacruz (1998) “Evaluation ofRADARSAT for Land Use and Land Cover Dynamics in the Southwestern Brazilian Amazon State ofAcre”, Canadian Journal of Remote Sensing, Vol. 24, No. 4, pp. 350-359 Leckie D.G. (1990) "Synergism of synthetic aperture radar and visible/infrared data for forest typediscrimination", Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 9, pp. 1237-1246(CCRS #: 1077172) Novo, E.M.L. de M., M.P. de F. Costa, and J.E. Mantovani (1998) “RADARSAT Exploratory Survey

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on Macrophyte Biophysical Parameteres in Tropical Reservoirs”, Canadian Journal of RemoteSensing, Vol. 24, No. 4, pp. 367-375 Paris J.F. and H.H. Kwong (1988). "Characterization of vegetation with combined Thematic Mapper(TM) and Shuttle Imaging Radar (SIR-B) image data", Photogrammetric Engineering and RemoteSensing, Vol. 54, No. 8, pp. 1187-1193 (CCRS #: 1060911) Richards J.A. (1990) "Radar backscatter modelling of forests: a review of current trends",International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1299-1312 (Special Issue onMicrowave Signatures of Forest) (CCRS #: 1076724) Sieber A.J. (Guest Editor) (1990) "Special Issue: International Forest Signature Workshop",International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1093 ff. Sun G., D.S. Simonett, and A.H. Strahler (1991) "A radar backscattering model for discontinuousforest canopies", IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 4, pp. 639-650 (CCRS #: 1082074) Ulaby, F.T., K. Sarabandi, K. McDonald, M. Whitt, and M.C. Dobson (1990) “Michigan microwavecanopy scattering model”, International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1223-1253 Ulaby F.T. and M.C. Dobson (1989). "Handbook of radar scattering statistics for terrain", ArtechHouse, Norwood, MA. Ulaby F.T., R.K. Moore, and A.K. Fung (1986) Microwave Remote Sensing: Active and Passive, Vol.III, Artech House Inc., Norwood, MA Way J., J. Paris, E. Kasischke, C. Slaughter, L. Viereck, N. Christensen, M.C. Dobson, F. Ulaby, andJ. Richards (1990) "The effect of changing environmental conditions on microwave signatures offorest ecosystems: Preliminary results of the March '88 Alaska aircraft SAR experiment",International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1119-1144 (CCRS #: 1076417) Yong Wang and J.M. Melack (1994) “Canopy penetration study for tropical rainforests: Modeledradar backscatter from Amazon floodplain forests at C-, L-, and P-band”, Proceedings of IGARSS’94, Pasadena, California (USA), Vol. 2, pp.1060-1062

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Radar Geology References - Références radar en géologie

Abdelsalam M.G. and R.J. Stern (1996) "Mapping Precambrian structures in the Sahara Desert withSIR-C/X-SAR radar: The Neoproterozoic Keraf Suture NE Sudan", Journalof Geophysical Research,Vol. 101, No. E10, pp. 23,063-23,076 Abdelsalam M.G., R.J. Stern, H. Schandelmeier, and M. Sultan (1995) "Deformational history of theNeoptoterozoic Keraf Zone in NE Sudan revealed by shuttle Imaging Radar", Journal of Geology,Vol. 103, No. 5, pp. 475-491 Arvidson R.E., M.K. Shepard, E.A. Guinness, S.B. Petroy, J.J. Plaut, D.L. Evans, T.G. Farr, R.Greeley, N. Lancaster, and L.R. Gaddis (1993) "Characterization of lava flow degradation in thePisgah and Cima volcanic fields, California using Landsat Thematic Mapper and AIRSAR data",Geological Society of America Bulletin, Vol. 105, pp. 175-188 Arvidson R.E., V.R. Baker, C. Elachi, R.S. Saunders, and J.A. Wood (1991) "Magellan: Initialanalysis of Venus surface modification", Science, Vol. 252, pp. 270-275 Blom R.G. (1988) "Effects of Variation in Look Angle and Wavelenght in Radar Images of Volcanicand Aeolian Terrains, or Now You See It, Now You Don’t", International Journal of Remote Sensing,Vol. 9, pp. 945-965 Budkewitsch P. and M.A. D'Iorio (1997) "Contributions Toward Understanding C-band SAR Data forLithological Discrimination and Structural Mapping in the Canadian Arctic", Proceedings of the 12thInternational Coference on Applied Geologic Remote Sensing, Denver, Colorado (USA), 17-19November, pp. I-38 - I-41 Budkewitsch P., M.A. D’Iorio, and J.C. Harisson (1996-a) “An Examination of The RelationshipBetween Lithology and Radar Signatures in Arctic Environments: Preliminary Results From BathurstIsland, N.W.T.”, Current Research 1996-B, Geological Survey of Canada, pp. 67-72 http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/nwt/rnwt01_e.html Budkewitsch P., M.A. D'Iorio, and J.C. Harrison (1996-b) “C-band radar signatures of lithology inarctic environments: preliminary results from Bathurst Island, Nunavut”, Current Research 1996-B,Geological Survey of Canada, pp. 67-72 http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/nwt/rnwt01_e.html Budkewitsch P., M.A. D'Iorio, and J.C. Harrison (1996-c) “SAR Expressions of Geology in theCanadian Arctic”, Proceedings for the 26th International Symposium on Remote Sensing ofEnvironment / 18th Symposium of the Canadian Remote Sensing Society, Vancouver (Canada), 25-29 March 1996, pp. 88-91. Campbell B.A., S.H. Zisk, and P.J. Mouginis-Mark (1989) "A quad-pol radar scattering model foruse in remote sensing of lava flow morphology", Remote Sensing of Environment, Vol. 30, pp. 227-237 Campbell D.B., N.J.S. Stacy, W.I. Newman, R.E. Arvidson, E.M. Jones, G.S. Musser, A.Y. Roper,and C. Schaller (1992) "Magellan observations of extended impact crater related features on thesurface of Venus", Journal of Geophysical Research - Planets, Vol. 97, (NE10), pp. 16249-16277 Coltelli M., G. Fornaro, G. Franceschetti, R. Lanari, M. Migliaccio, J.R. Moreira, K.P. Papathanassiou,G. Puglisi, D. Riccio, and M. Schwabisch (1996). "SIR-C/X-SAR multifrequency multipassinterferometry: A new tool for geological interpretation", Journal of Geophysical Research, Vol.101, No. E10, pp. 23,127-23,148

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Dabbagh A., K. Al-Hinai, and A. Khan (1997) "Detection of sand covered geologic features in theArabian Peninsula using SIR- C/X-SAR data", Remote Sensing of Environment, Vol 59, No. 2, pp.375-383 Davis P. A., C.S. Breed, J.F. McCauley, and G.G. Schaber (1993) "Surficial geology of the Safsafregion, south-central Egypt, derived from remote-sensing and field data", Remote Sensing ofEnvironment, Vol. 46, pp. 183-203 Dean K.G. and L.A. Morrissey (1988) "Detection and identification of arctic landforms: Anassessment of remotely sensed data", Photogrammetric Engineering and Remote Sensing, Vol. 54,No. 3, pp. 363-371 (CCRS #: 1064215) Deslandes S. and Q.H.J. Gwyn (1991) "Évaluation de SPOT et SEASAT pour la cartographie deslinéaments: Comparaison basée sur l'analyse de spectres de Fourier, Journal canadien detélédétection, Vol. 17, No. 2, pp. 98-109 (CCRS #: 1081884) D'Iorio M.A., P. Budkewitsch, and N.N. Mahmood (1997) "Practical Considerations for GeologicalInvestigations using RADARSAT-1 Stereo Image Pairs in Tropical Environments", Geomatics in theEra of RADARSAT, Proceedings, Ottawa, Canada, 27-30 May 1997, Paper #233, pp. 9 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2239 D'Iorio M., B. Rivard, and P. Budkewitsch (1996) “Use of SAR Wavelength and PolarizationInformation for Geological Interpretation of Semi-arid Terrain”, Canadian Journal of RemoteSensing, Vol. 22, No. 3, pp. 305-316 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1528 Evans D. (1988) "Multisensor classification of sedimentary rocks", Remote Sensing of Environment,Vol. 25, No. 2, pp. 129-144

Evans D.L. (1992-a) "Current Status and Future Developments in Radar Remote Sensing", ISPRSJournal of Photogrammetry and Remote Sensing, Vol. 47. pp. 79-99

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Lizeca J. L. “San Agustin, Bolivia; Multi-Andean Project, Bolivia; Mineral Exploration with RADARSAT Images” http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/bol/bolimap_e.html Lizeca J. L. , W.M. Moon, C.A. Hutton, L. Wu, and C.W. Lee (1999) “Investigation of PastosGrandes (Bolivia) Volcanic Features with RADARSAT”, IGARSS’99, Hamburg, Germany, 28 June – 2July 1999 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4734.pdf Lowman P.D. Jr. (1991) "Original shape of the Sudbury Structure, Canada: A study with airborneimaging radar", Canadian Journal of Rem. Sensing, Vol. 17, No. 2, pp. 152-161 (CCRS #:1081888) MacKay M. and P. Mouginis-Mark (1997) "The effect of varying acquisition parameters on theinterpretation of SIR-C radar data: The Virunga volcanic chain", Remote Sensing of Environment,Vol. 59, No. 2, pp. 321-336 Massonet D., M. Rossi, C. Carmona, F. Adragna, G. Peltzer, K. Feigl, and T. Rabaute (1993) "TheDisplacement Field of the Landers Earthquake Mapped by Radar Interferometry", Nature, 364: pp.138-142 Misra K.S., V.R. Slaney, D. Graham, and J. Harris (1991) "Mapping of basement and other tectonicfeatures using SEASAT and Thematic Mapper in hydrocarbon-producing areas in the WesternSedimentary Basin of Canada", Canadian Journal of Remote Sensing, Vol. 17, No. 2, pp. 137-151(CCRS #: 1081887)


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Mouginis-Mark P.J. (1995-a) "Preliminary observations of volcanoes with the SIR-C radar", IEEETransactions on Geoscience and Remote Sensing, Vol. 33, No. 4, pp. 934-941 Mouginis-Mark P.J. (1995-b) "Analysis of volcanic hazards using radar interferometry", EarthObservation Quarterly, Vol. 47, pp. 2-10 Mouginis-Mark P.J. (1994) "Volcanic hazards revealed by radar interferometry", Geotimes, Vol. 39,No. 7, pp. 11-13 Mouginis-Mark P.J., D.C. Pieri, and P.W. Francis (1993-a) "Volcanoes", in Atlas of satelliteobservations related to global change, R.J. Gurney, J.L. Foster and C.L. Parkinson, Eds., CambridgeUniversity Press, pp. 341-357 Mouginis-Mark P.J. and H. Garbeil (1993-b) "Digital topography of volcanoes from radarinterferometry: An example from Mt. Vesuvius, Italy", Bulletin Volcanology, Vol. 55, pp. 566-570 Mouginis-Mark P.J. and P. W. Francis (1992) "Satellite observations of active volcanoes: Prospectsfor the 1990's", Episodes, 15, pp. 46-55 Mouginis-Mark P.J., S. Rowland, R. Francis, T. Friedman, H. Garbeil, J. Gradie, S. Self, L. Wilson, J.Crisp, L. Glaze, K. Jones, A. Kahle, D. Pieri, H. Zebker, C. Wood, W. Rose, J. Adams, and R. Wolff(1991) "Analysis of active volcanoes from the Earth Observing System", Remote Sensing ofEnvironment, Vol. 36, pp. 1-12 Mussakowski R., N.F. Trowell, and K.B. Heather (1991) "Digital integration of remote sensing andgeoscience data for the Goudreau-Lochalsh area, Wawa, Ontario", Canadian Journal of RemoteSensing, Vol. 17, No. 2, pp. 162-173 (CCRS #: 1081889) Rheault M., R. Simard, C. Garneau, and V.R. Slaney (1991) "SAR-Landsat TM-geophysical dataintegration utility of value-added products in geological exploration", Canadian Journal of RemoteSensing, Vol. 17, No. 2, pp. 185-190 (CCRS #: 1081891) Rowland S. K., G.A. Smith, and P.J. Mouginis-Mark (1994) "Preliminary ERS-1 observations ofAlaskan and Aleutian volcanoes", Remote Sensing Environment, Vol. 48, No. 33, pp. 358-369 Sabins F.F., Jr. (1997) "Remote Sensing: Principles and Interpretation", 3rd Edtition, W.H. Freemanand Co., New York Saint-Jean R. and V. Singhroy (2000) “Hydrogeological Mapping in the Semi-arid Environment ofEastern Jordan Using Airborne Multipolarized Radar Images”, First Joint World Congress onGroundwater, Fortaleza - Ceara, Brasil, 31 July to 4 August 2000 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/13044.pdf Saunders, R.S., A.J. Spear, P.C. Allin, R.S. Austin, A.L. Berman, R.C. Chandlee, J. Clark, A.V. deCharon, E. DeJong, D. Griffith, J.M. Gunn, S. Hensley, W. Johnson, C.E. Kirby, K.S. Leung, D.T.Lyons, G. Michaels, J. Miller, R. Morris, A.D. Morrison, R.G. Pierson, J. Scott, S. Shaffer, J. Slonski,E.R. Stofan, and S.D. Wall (1992) "Magellan mission summary", Journal of Geophysical Research,Vol. 97, No. E8, pp. 13067-13090 Saunders, R.S., R.E. Arvidson, J.W. Head III, G.G. Schaber, E.R. Stofan, and S.C. Solomon (1991)."An Overview of Venus Geology", Science, Vol. 252, pp. 249-252 Schaber G.G., J. McCauley, and C. Breed (1997) "The use of multiwavelength and polarimetric SIR-C/X-SAR data in geologic studies of Bir Safsaf, Egypt", Remote Sensing of Environment, Vol. 59,



No. 2, pp. 337-363 Singhroy, V., “RADARSAT Data Integration in the Sudbury Mining District”, http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/geology/sudbury/sudburye.html Singhroy V., J.E. Loehr, and A.C. Correa (2000) “Landslide Risk Assessment with High SpatialResolution Remote Sensing Satellite Data”, IGARSS 2000, Honolulu, Hawaii (USA), 24-28 July 2000http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/13012.pdf Singhroy V. and R. Saint-Jean (1999) “Effects of relief on the selection of RADARSAT-1 incidenceangle for geological applications”, Canadian Journal of Remote Sensing, Vol. 25, No. 3, pp. 211-217 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4723.pdf Singhroy V. , R. Saint-Jean, and B. Rivard (1995) “SAR Integration Techniques for GeologicalInvestiagtions: Case studies in Jordan, Canada, and Guyana”, 17th Canadian Symposium onRemote Sensing, Proceedings, Saskatoon, Canada, 13-15 June 1995, pp. 734-741 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1661 Thomas J., W. Kober, and F. Leberl (1991) "Multiple image SAR shape-from-shading",Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 1, pp. 51-59 (CCRS #: 1078829) Ulaby F.T., T. Bengal, M.C. Dobson, J. East, J. Garvin, and D. Evans (1990) "Microwave dielectricproperties of dry rocks", IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 3, pp.325-335 Ulaby F.T., R.K. Moore, and A.K. Fung (1982) "Microwave Remote Sensing", Vol. II, Addison-Wesley Pub. Co. Reading, MA, 1064p. Van Zyl J.J. (1990) "A Technique to Calibrate Polarimetric Radar Images Using Only ImageParameters and Trihedral Corner Reflectors", IEEE Transactions on Geoscience and RemoteSensing, GE-28: 337-348 Wall S.D., T.G. Farr, J.P. Muller, P. Lewis, and F.W. Leberl (1991) "Measurement of surfacemicrotopography", Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 8, pp. 1075-1078 Weeks R., M. Smith, K. Pak, and A. Gillespie (1997) "Roughness of geologic surfaces fromforeground/background analysis of SIR-C and AIRSAR data", Remote Sensing of Environment, Vol.59. No. 2, pp. 384-397 Weeks R.J., M. Smith, K. Pak, W.-H. Li, A. Gillespie, and B. Gustafson (1996) "Surface roughness,radar backscatter, and visible and near-infrared reflectance in Death Valley, California", Journal ofGeophysical Research, Vol. 101, No. E10, pp. 23,077-23,090


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Interferometry References - Références de l'interférometie

Atlantis Scientific Inc. (1998) “Digital Elevation Model for an Area of Western Argentina, derivedfrom RADARSAT Interferometric SAR Data”, Commissioned by CCRS for GlobeSAR-2, 26 Nov. 1998 CCRS C & X Band SAR overview, “CCRS Convair 580” http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/airborne/sarbro/sbc580_e.html CCRS C & X Band SAR Overview, “Research of the CCRS SAR System” http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/airborne/sarbro/sbinter_e.html CCRS C & X band SAR, “CCRS Convair 580” http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/airborne/sarbro/sbc580_e.html CCRS R&D Support Programs – RUDP, “The InSAR Workstation and the RUDP Program” Cumming I., J.-L. Valero, P. Vachon, K. Mattar, D. Geudtner, and L. Gray (1996) “Glacier FlowMeasurements with ERS Tandem Mission Data”, ESA Workshop on Applications of ERS SARInterferometry, Fringe'96, Remote Sensing Laboratories, University of Zurich, Switzerland, Sept30-Oct 2, 1996. ESA SP-406, March 1997, pp. 353-362 http://www.ee.ubc.ca/sar/FRINGE96/GLACIER/GL.htm Ehrismann J., M. van der Kooij, and B. Hulshof (2001) “Commercial Applications of SARInterferometry for Change Detection”, Atlantis Scientific Inc. http://www.atlsci.com/library/commercial_apps_of_SAR_interferometry_for_change_detection.htm Fatland D.R., “STEP's Chitina Interferogram”, Science, Technology and Education Program, AlaskaSAR Facility, Geophysical Institute, University of Alaska http://www.asf.alaska.edu/step/chitina_inf.html Fatland D. R., “Digital Elevation Models / Topographic Applications”, Alaska SAR Facility http://www.asf.alaska.edu/step/insar/applications.html#dems Ferretti A., C. Prati, and R. Focca (2001) “Permanent scatterers in SAR interferometry”, IEEETransactions on Geoscience and Remote Sensing, Vol. 39, No. 1, Jan. 2001, pp.8-20 Ferretti A., C. Prati, F. Rocca, and A. M. Guarnieri (1997) “Multi-baseline SAR Interferometry forAutomatic DEM Reconstruction”, 3rd ERS SYMPOSIUM, Florence, 17 - 21 March 1997 http://earth1.esrin.esa.it/l2/10/symposia Geile, W. (2001) “IFSAR-Demo CD”, Geomatics Consulting, Bad Krozingen, Germany Geudtner D., P.W. Vachon, K. Mattar, and A.L. Gray (1998) “RADARSAT Repeat-Pass SARInterferometry”, IGARSS'98, Seattle, WA, USA, 6-10 July 1998, 3 p. http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3564.pdf Gray A.L., K. Mattar, and M. van der Kooij (1995) "Cross-Track and Along-Track AirborneInterferometric SAR at CCRS", 17th Canadian Symposium on Remote Sensing, Proceedings,Saskatoon, Canada, 13-15 June 1995, pp. 232-237 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2046 Gray A.L., K. Mattar, and P.J. Farris-Manning (1992) “Airborne SAR Interferometry for TerrainElevation”, International Geoscience and Remote Sensing Symposium - IGARSS '92, Proceedings,Houston, TX (USA), 26-29 May 1992, Vol. 2, pp. 1589-1591 (CCRS #: 1089116)

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Radar Mapping References - Références radar en cartographie

Bloom A.L., E.J. Fielding, and X.-Y. Fu (1988) “A Demonstration of Stereo-Photogrammetry withCombined SIR-B and Landsat-TM Images”, International Journal of Remote Sensing, Vol. 9, No. 5,pp. 1023-1038 Buchroithner M. (1989) “Stereo-viewing from Space”, Advances in Space Research, Vol. 19, No.1,pp. 29-40 Carlson, G.E. (1973) “An Improved Single Flight Technique for Radar Stereo”, IEEE Transactions onGeoscience Electronics, GE-11, pp. 199-204 Centre National d’Études Spatiales (CNES) (1980) «Le mouvement de véhicule spatial en orbite», Toulouse, France, 1031 pages Cyr I. and Toutin Th. (2001) "RADARSAT-1 Stereo Advisor on CCRS Web", Canadian Journal ofRemote Sensing, Technical Note, Vol. 27, No. 1, pp. 62-66 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4837.pdf Domik G., F. Leberl, and J. Cimino (1988) “Dependence of Image Grey Values on Topography inSIR-B Images”, International Journal of Remote Sensing, Vol. 19, No. 5, pp. 1013-1022 Domik G. (1983) “Evaluation of Radar Stereoviewability by Means of Simulation Techniques”,Proceedings IGARSS’84, Paris, France, ESA-SP-215, pp. 623-646 Escobal P.R. (1965) “Methods of Orbit Determination”, Krieger Publishing Company, Malabar,Florida, USA, 479 pages Frankot T.R. and R. Chellapa (1990) “Estimation of Surface Topography from SAR Imagery UsingShape from Shading Techniques”, Artificial Intelligence, Vol. 43, pp. 271-310 Frankot T.R. and R. Chellapa (1988) “A Method for Enforcing Integrability in Shape from Shading”,IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 4, 439-451 Fullerton J.K., F. Leberl, and R.E. Marque (1986) “Opposite Side SAR Image Processing for Stereo-viewing”, Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 9, pp. 1487-1498 Gabriel A.K., R. Goldstein, and H. Zebker, (1989). “Mapping Small Elevation Changes Over LargeAreas: Differential Radar Interferometry”, Journal of Geophysical Research, Vol. 94 No. B7, pp.9183-9191 Gabriel A.K. and R.M. Goldstein (1988) “Crossed-Orbit Interferometry: Theory and ExperimentalResults from SIR-B”, International Journal of Remote Sensing, Vol. 9, No. 5, pp. 857-872 Goldstein R.M., H. Engelhardt, B. Kamb, and R.M. Frolich (1993) “Satellite Radar Interferometry forMonitoring Ice Sheet Motion: Application to an Antartic Ice Stream”,Science, Vol. 262, pp. 1525-1530 Goldstein R.M., H. Zebker, and C. Werner (1988) “Satellite Radar Interferometry: Two-Dimensional Phase Unwrapping”, Radio Science, Vol. 23, No. 4, pp. 713-720 Graham L.C. (1974) “Synthetic Interferometer Radar for Topographic Mapping”, Proceedings of theIEEE, Vol. 62 No. 6, pp. 763-768.

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“The Displacement Field of the Landers Earthquake Mapped by Radar Interferometry”, Nature, Vol.364, pp. 138-142 Massonnet D. and T. Rabaute (1993-b) “Radar Inteferometry: Limits and Potential”, IEEETransactions on Geoscience and Remote Sensing, Vol, 31, No. 2, pp. 455-464 Moore R.K. (1969) “Heights from Simultaneous Radar and Infrared”, Photogrammetric Engineering,Vol. 5, No. 7, pp. 649-651 Paquerault S. et H. Maître (1997) «La radarclinométrie», Bulletin de la Societé Française dePhotogrammétrie et de Télédétection, Vol. 148, pp. 20-29 PCI Geomatics Inc. (1993) PCI User Manual, Chapter 9 Polidori L. et Th. Toutin (1998) «Cartographie du relief par imagerie radar : l’état de l’art», Bulletinde la Societé Française de Photogrammétrie et de Télédétection, Vol. 152, No. 4, pp. 12-23 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=4626 Polidori L. (1996). «Cartographie radar», Gordon and Breach Science Publishers, Amsterdam, TheNetherlands, 287 pages. Raggam H., K. Gutjahr, and A. Almer (1997). “MOMS-2P und RADARSAT: Neue Sensoren zurstereometrischen Geländemodellerstellung”, Vermessung und Geoinformation, Heft Vol. 4/97, pp.267-280 Raggam J. and A. Almer (1996). “Assessment of the Potential of JERS-1 for Relief Mapping UsingOptical and SAR Data”, International Archives of Photogrammetry and Remote Sensing, Vienna,Austria, 31 Vol. (B4), pp. 671-676 Raggam J., A. Almer, and D. Strobl (1994) “A Combination of SAR and Optical Line Scanner Imagerfor Stereoscopic Extraction of 3-D Data”, ISPRS Journal of Photogrammetry and Remote Sensing,Vol. 49, No. 4, pp. 11-21 Ramapriyan H., J. Strong, Y. Hung, and C. Murray (1986) “Automated Matching Pairs of SIR-BImages for Elevation Mapping”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 24, No.4, pp. 462-472 Rodgers A.E.E. and R.P. Ingalls (1969) “Venus Mapping: The Surface Reflectivity by RadarInterferometry”, Sciences, Vol. 165, pp. 797-799 Rosenfield G.H. (1968) “Stereo Radar Techniques”, Photogrammetric Engineering, Vol.34, pp. 586-594 Sylvander S., D. Cousson et P. Gigord (1997) «Étude des performances géométriques des imagesRADARSAT», Bulletin de la Société Française de Photogrammétrie et de Télédétection, Vol. 148,pp. 57-65 Thomas J. and W. Kober (1990) “Radarclinometry – Shape from Shading: Generalized N-ImageAlgorithm”, Sections 15.4 to 15.7 of Radargrammetric Image Processing by F. Leberl, ArtechHouse, Norwood, USA, 435-551 Thomas J., W. Kober, and F. Leberl (1989) “Multiple-Image SAR Shape from Shading”, ProceedingsIGARSS’89, Vancouver, Canada, 10-14 July, pp. 592-596



Toutin Th. , K.E. Mattar, B. Brisco, A.L. Gray, y M.J. Manore (2001-a) “Producción DEM conRADARSAT: Panorama y Ejemplos?, accepted for publication in SELPER Journal, 2001 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/13021.pdf Toutin Th. (2001-b) "Potential of Road Stereo Mapping with RADARSAT Images", accepted atPhotogrammetric Engineering and Remote Sensing, Vol. 67 , 27 p. http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4765.pdf Toutin Th. and A.L. Gray (2000-a) “State-of-the-art of extraction of elevation data using satelliteSAR data”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 55, No. 1, pp. 13-33 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4751.pdf Toutin Th. (2000-b) “Stereo-Mapping with SPOT-P and ERS-1 SAR Images”, International Journalof Remote Sensing, Vol. 21, No. 8, pp. 794-796 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3662.pdf Toutin Th. and S. Amaral (2000-c) “Stereo RADARSAT Data for Canopy Height in BrazilianForests”, Canadian Journal for Remote Sensing, Vol. 26, No. 3, pp. 189-199 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4753.pdf Toutin Th. (2000-d) "Evaluation of Radargrammetric DEM from RADARSAT Images in High ReliefAreas", IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, pp. 782-789 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4752.pdf Toutin Th. (2000-e) "Elevation Modelling from Satellite Data", Encyclopedia of AnalyticalChemistry: Instrumentation and Application, edited by: R.A. Meyers , Vol. 10, John Wiley & SonsLtd., Chichester, UK, pp. 8543-8572 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4622.pdf Toutin Th. (1999) “Error Tracking of Radargrammetric DEM from RADARSAT Images”, IEEETransactions on Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2227-2238 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3604.pdf Toutin Th. (1998) "Évaluation de la précision géométrique des images de RADARSAT", Journalcanadien de télédétection, Vol. 24, No. 1, pp. 80-88 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3277.pdf Toutin Th. (1996) “Opposite-side ERS-1 SAR Stereo Mapping Over Rolling Topography”, IEEETransactions on Geoscience and Remote Sensing, Vol. 34, No. 2, pp. 543-549 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/1623.pdf Toutin Th. (1995) “Generating DEM from Stereo Images with a Photogrammetric Approach:Example with VIR and SAR Data, EARSeL Journal Advances in Remote Sensing, Vol. 4, No. 2, pp.110-117 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1222 Twu Z.-G. and I. Dowman (1996) “Automatic Height Extraction from ERS-1 SAR Imagery”,International Archives of Photogrammetry and Remote Sensing, Vol, 31, No. B2, pp. 380-383 Wildey R.L. (1986) “Radarclinometry for the Venus Radar Mapper”, Photogrammetric Engineeringand Remote Sensing, Vol. 52, No. 1, pp. 41-50 Wildey R.L. (1984) “Topography from Single Radar Images”, Sciences, 224, 153-156













Yelizavetin I.V. and Ye. A. Ksenofontov (1996) “Precision Terrain Measurement by SARInterferometry”, Mapping Science sand Remote Sensing, Vol. 33, No. 1, pp. 1-19 Yelizavetin I.V. (1993) “Digital Terrain Modeling from Radar Image Stereopairs”, Mapping Scienceand Remote Sensing, Vol.30, No. 2, pp. 151-160 Zebker H.A., C. Werner, P.A. Rosen, and S. Hensley (1994) “Accuracy of Topographic Maps Derivedfrom ERS-1 Interferometric Radar”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 32,No. 4, pp. 823-836


Radar Oceanography References - Références radar en océanographie

General ocean remote sensing (including SAR) texts: Henderson F.M. and A.J. Lewis (editors) (1998) "Principles and Applications of Imaging Radar",Manual of Remote Sensing, Third Edition, Volume 2, ASPRS, John Wiley and Sons, Toronto, 866 p. Ikeda M. and F.W. Dobson (editors) (1995) "Oceanographic Applications of Remote Sensing", CRCPress, Boca Raton, FL Marine Environmental Data Service (MEDS), Department of Fisheries and Oceans (DFO), Canada,“Ocean Monitoring Workstation Products” http://www.meds-sdmm.dfo-mpo.gc.ca/meds/Databases/Satellite/omw/Products_e.htm Ships: Lyden J.D., R.R. Hammond, D.R. Lyzenga, and R.A. Shuchman (1988) "Synthetic aperture radarimaging of surface ship wakes", Journal of Geophysical Research, Vol. 93, No. C10, pp. 12293-12303 (CCRS #: 1067583) Milgram J.H. (1988) "Theory of radar backscatter from short waves generated by ships, withapplication to radar (SAR) imagery", Journal of Ship Research, Vol. 32, No. 1, pp. 54-69 (CCRS #:1065611) Vachon P.W. , S. J. Thomas, J. Cranton, H. Edel, and M.D. Henschel (2000) “Validation of ShipDetection by the RADARSAT Synthetic Aperture Radar and the Ocean Monitoring Workstation”, Canadian Journal of Remote Sensing, Vol. 26, No. 3, p. 200-212 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3533.pdf Vachon P.W., J.W.M. Campbell, C. Bjerkelund, F. W. Dobson, and M.T. Rey (1997) “Ship detectionby the RADARSAT SAR: Validation of detection model predictions”, Canadian Journal of RemoteSensing, Vol. 23, No. 1, pp 48-59 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/1849.pdf Ocean Waves: Chunchuzov I. , P.W. Vachon, and X. Li (2000-a) “Analysis and Modelling of Atmospheric GravityWaves Observed in RADARSAT SAR Images”, Remote Sensing of Environment, Vol. 74, No. 3, pp.343-361 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4696.pdf Chunchuzov I., P.W. Vachon, and B. Ramsay (2000-b) “Detection and Characterization ofMesoscale Cyclones in RADARSAT Synthetic Aperture Radar Images of the Labrador Sea”, CanadianJournal of Remote Sensing, Vol. 26, No. 3, 2000, pp. 213-230 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3536_1.pdf Hasselmann S., C. Brüning, K. Hasselmann, and P. Heimbach (1996) “An improved algorithm forthe retrieval of ocean wave spectra from synthetic aperture radar image spectra”, Journal ofGeophysical Research, Vol. 101, No. C7, pp. 16,615-16,629 Hasselmann K. and S. Hasselmann (1991) "On the nonlinear mapping of an ocean wave spectruminto a synthetic aperture radar image spectrum and its inversion", Journal of Geophysical Research,

Page 1 of 4Bibliography - Radar Oceanography References

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Vol. 96, No. C6, pp. 10713-10729 (CCRS #:1081869) Hasselmann K. and W. Alpers (1986) "The response of synthetic aperture radar to ocean surfacewaves", Chapter 26, pp. 393-401, in: Phillips O.M. & K. Hasselmann (eds.) Wave Dynamics andRadio Probing of the Ocean Surface, Plenum Press, New York, London Holt B. (1988) "Introduction: Studies of ocean wave spectra from the Shuttle Imaging Radar - Bexperiment", Journal of Geophysical Research, Vol. 93, No. C12, pp. 15365-15366 Phillips O.M. and K. Hasselmann (eds.) (1986) "Wave Dynamics and Radio Probing of the OceanSurface", Plenum Press, New York, London Raney R.K. and P.W. Vachon (1988) "Synthetic aperture radar imaging of ocean waves from anairborne platform: Focus and tracking issues", Journal of Geophysical Research, Vol. 93, No. C10,pp. 12475-12486 (CCRS #: 1067591) Rufenach C.L., R.A. Shuchman, and N.P. Malinas (1991) "Ocean wave spectral distortion inairborne SAR imagery during the Norwegian continental shelf experiment of 1988", Journal ofGeophysical Research, Vol. 96, No. C6, pp. 10453-10466 (CCRS #: 1081864) Vachon P.W. , J.W. Campbell, and F.W. Dobson (1999) “Validation of Along-Track InterferometricSAR Measurements of Ocean Surface Waves”, IEEE Transactions on Geoscience & Remote Sensing,Vol. 37, No. 1, 1999, pp. 150-162 Vachon, P.W., H.E. Krogstad, and J.S. Paterson (1994) “Airborne and Spaceborne SARObservations of Ocean Waves”, Atmosphere-Ocean, Vol. 32, No. 1, pp. 83-112 Vachon P.W. and R.K. Raney (1991) "Resolution of the ocean wave propagation direction in SARimagery", IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 1, pp. 105-112(CCRS #: 1078811) Vachon P.W. and R.K. Raney (1989) "Estimation of the SAR system transfer function throughprocessor defocus", IEEE Transactions on Geoscience and Remote Sensing, Vol. 27, No. 6, pp. 702-708 (CCRS #: 1073129) Vesecky J.F., R.H. Stewart, R.A. Shuchman, H.M. Assal, E.S. Kasischke, and J.D. Lyden (1986)"One the ability of synthetic aperture radar to measure ocean waves", Chapter 27, pp. 403-421, in:Phillips O.M. & K. Hasselmann (eds.) Wave Dynamics and Radio Probing of the Ocean Surface,Plenum Press, New York, London Coastal: Lee J.S. and I. Jurkevich (1990) "Coastline detection and tracing in SAR images", IEEETransactions on Geoscience and Remote Sensing, Vol. 28, No. 4, pp. 662-668 (CCRS #: 1076849) Manore, M.J., P.W. Vachon, C. Bjerkelund, H.R. Edel, and B. Ramsey (1998) “Operational Use ofRADARSAT in the Coastal Zone: The Canadian Experience”, 27th International Symposium onRemote Sensing of the Environment, Tromso, Norway, 8-12 June 1998, pp. 115-118 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3479.pdf Werle D. (1991) "Coastal zone sensitivity investigations and SAR: The Northumberland Coast casestudy", Research Report, Environment Canada, Dartmouth, NS, Canada, 88 p. (CCRS #: 1081703)



Slicks: CCRS Tutorial: Fundamentals of Remote Sensing; Section 5.9.3: Applications, Oceans and CoastalMonitoring, “Oil Spill Detection” http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_1_e.html Espedal H.A. and T. Wahl (1999) “Satellite SAR oil spill detection using wind history information”,International Journal of Remote Sensing, Vol. 20, No. 1, pp 49-65 Huhnerfuss H., W. Alpers, and F. Witte (1989) "Layers of different thickness in mineral oil spillsdetected by grey level textures of real aperture radar images", International Journal of RemoteSensing, Vol. 10, No. 6, pp. 1093-1099 (CCRS #: 1071170) Werle, D., B. Tittley, E. Theriault, and B. Whitehouse (1997) “Using RADARSAT SAR Imagery toMonitor the Recovery of the Irving Whale Oil Barge”, Proceedings Geomatics in the Era ofRADARSAT, GER’97, Ottawa, May 25-30, 1997 http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/marine/irving/irving_e.html Internal Waves: Liu A.K., Y.S. Chang, M.-K. Hsu, and N.K. Liang (1998) “Evolution of nonlinear internal waves inthe East and South China Seas”, Journal of Geophysical Research, Vol. 103, No. C4, pp. 7995-8008 Shuchman R.A., D.R. Lyzenga, B.M. Lake, B.A. Hughes, R.F. Gasparovic, and E.S. Kasischke(1988) "Comparison of joint Canada-US ocean wave investigation project Synthetic aperture radardata with internal wave observations and modeling results", Journal of Geophysical Research, Vol.93, No. C10, pp. 12283-12291 (CCRS #: 1067582) Wind: Johannessen J.A., R.A. Shuchman, O.M. Johannessen, K.L. Davidson, and D.R. Lyzenga (1991)“Synthetic aperture radar imaging of upper ocean circulation features and wind fronts", Journal ofGeophysical Research, Vol. 96, No. C6, pp. 10411-10422 (CCRS #: 1081861) Vachon P. and F.W. Dobson (2000) “Wind Retrieval from RADARSAT SAT Images: Selection of aSuitable C-band HH Polarization Wind Retrieval Model”, Canadian Journal of Remote Sensing, ADROFinal Sym. Special Issue, Vol. 26, No. 4, pp. 306-313 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4650.pdf Vachon P.W. and F.W. Dobson (1996) “Validation of wind vector retrieval from ERS-1 SAR imagesover the ocean”, The Global Atmosphere and Ocean System, Vol. 5, pp. 177-187 Bottom Topography: Vogelzang J. (1997) “Mapping submarine sand waves with multiband imaging radar, 1, Modeldevelopment and sensitivity”, Journal of Geophysical Research, Vol. 102, No. C1, pp. 1163-1182 Vogelzang J. (1989) "The mapping of bottom topography with imaging radar - A comparison of thehydrodynamic modulation in some existing models”, International Journal of Remote Sensing, Vol.19, No. 10, pp. 1503-1518 (CCRS #: 1072759) Ocean Currents: Chubb S.R., G.R. Valenzuela, and D.A. Greenberg (1991) "Radar surface signatures based on the


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two-dimensional tidal circulation of Phelps Bank", IEEE Transactions on Geoscience and RemoteSensing, Vol. 29, No. 1, pp. 129-134 (CCRS #: 1078814) Johannessen J.A., R.A. Shuchman, D.R. Lyzenga, C. Wackerman, O.M. Johannessen, and P.W.Vachon (1996) “Coastal ocean fronts and eddies imaged with ERS-1 synthetic aperture radar”,Journal of Geophysical Research, Vol. 101, No. C3, pp. 6651-6667 RADARSAT Applications: Gagliardini D.A., J. Bava, J.A. Milovich, and L.A. Frulla (1999) “Contribution of SAR Images toStudy the Ocean Dynamics in the the South Atlantic Tropical Convergence Region”, Simposio FinalGlobeSAR 2, Buenos Aires, Argentina, 17-20 de Mayo 1999, pp. 191-198 Gray, A.L, P.W. Vachon, C.A. Bjerkelund, and M.J. Manore (1997) ”Mode Selection and ImageOptimization for Coastal, Ocean, and Ice Applications of RADARSAT Imagery”, InternationalSymposium, Geomatics in the Era of RADARSAT (GER'97), Ottawa, Canada, 25-30 May 1997, p. 13http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2277 Vachon, P. and R.B. Olsen, “RADARSAT SAR Mode Selection for Marine Applications: AmendmentsBased On Post-Launch Experience" : http://pcmas1.ccrs.nrcan.gc.ca/ccrsnew/rd/apps/marine/beam/beam_e.html Vachon P.W. and R. Olsen (1998-a) “RADARSAT - Which mode should I use?”, Backscatter, Official Magazine of the Alliance for Marine Remote Sensing (AMRS) Vachon P.W. and R.B. Olsen (1998-b) “RADARSAT SAR Mode Selection for Marine Applications:Amendments Based On Post-Launch Experience”, Backscatter, Marine Environmental Information &Technology, Newsletter of the Alliance for Marine Remote Sensing, pp. 14-20 Vachon, P.W. and R.B. Olsen (1995) "RADARSAT SAR mode selection for marine applications",Backscatter,Newsletter of The Atlantic Centre for Remote Sensing of the Oceans, Vol. 6, No. 3, pp.3-4 & 18. ERS-1 Applications: Johannessen J.A. (1991) "The Norwegian continental shelf experiment prelaunch ERS-1investigation", Journal of Geophysical Research, Vol. 96, No. C6, pp. 10409-10410, SpecialSection: NORCSEX, pp. 10409-10506 (CCRS #: 1081860)



http://pcmas1.ccrs.nrcan.gc.ca/ccrsnew/rd/apps/marine/beam/beam_e.html

Polarimetry References - Références en polarimétrie

Baronti S., F. Del Frate, P. Ferrazzoli, S. Paloscia, P. Pampaloni, and G. Schiavon (1995) “SARPolarimetric Features of Agricultural Areas”, International Journal of Remote Sensing, Vol. 14, pp.2639-2656 Boerner W-M., H. Mott, C.E. Livingstone, B. Brisco, R.J. Brown, and J.S. Paterson (1995)“Polarimetry in Remote Sensing - Basic and Applied Concepts”, American Society forPhotogrammetry and Remote Sensing Manual of Remote Sensing, Third Edition, Chapter 5 CAL Corporation (1996) "RADARSAT 2 Data Base Study Dual Polarization Option", Commissionedby the Canadian Space Agency, 128 p. plus Appendices Canada Centre for Remote Sensing (1995) “RADARSAT III - Phase 0 Report CCRS ApplicationStudies Accomplishments”, Commissioned by the Canadian Space Agency, 45 p. plus Appendices Evans D.L., T.G. Farr, J.J. van Zyl, and H.A. Zebker (1988) "Radar Polarimetry: Analysis Tools andApplications", IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 6, pp. 774-789 Jackson C., H. Rais, and B. Huxtable (1998) "Polarimetry and its use in automatic target detectionwith examples from Search and Rescue", Proceedings of SPIE, Vol. 3069, Orlando, FL, Apr. 22-25,1997 http://www.radarresources.com/cj_spie97.pdf Lukowski T.I. (2001) "Detection and Classification of Man-made objects in Polarimetric SARImagery", Demonstration of RADARSAT-2 Applications http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/r2demo/demo6/overview_e.html McNairn H. (2001) "Crop Identification and Condition Mapping using Polarimetric SAR Cata",Demonstration of RADARSAT-2 Applications http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/r2demo/demo5/overview_e.html NASA Goddard Space Flight Centre, Search and Rescue Mission Office "Beaconless Search orRemote Sensing" http://poes2.gsfc.nasa.gov/sar/becnless.htm Touzi R. (2001) "Polarimetric Radars for Ship Detection", Demonstration of RADARSAT-2Applications http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/r2demo/demo1/overview_e.html van Zyl J. and H. Zebker (1990) “Radar Polarimetry for Geoscience Applications”, in PolarimetricSAR Applications, Edited by F.T. Ulaby and C. Elachi, Artec House Inc., pp. 315-360 van Zyl J., R. Carande, Y. Lou, T. Miller, and K. Wheeler (1992) "The NASA/JPL Three-FrequencyPolarimetric AIRSAR System", IGARSS '92 Symposium, 26-29 May, pp. 649-651 van Zyl J.J. (1990) "Calibration of Polarimetric Radar Images Using Only Image Parameters andTrihedral Corner Reflector Responses", IEEE Transactions on Geoscience and Remote Sensing, Vol.GE28, pp. 337-348 van Zyl J.J., H.A. Zebker, and C. Elachi (1987) "Imaging Radar Polarization Signatures: Theory andObservation", Radio Science, Vol. 22, No. 4, pp. 529-543

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Van Zyl J.J., H.A. Zebker, and C. Elachi (1990) "Polarimetric SAR Applications", Chapter 7 in RadarPolarimetry for Geoscience Applications, edited by F.T. Ulaby and C. Elachi, Artech House,Norwood, MA, 02062, pp. 315-356 Zebker H.A. and Y. Lou (1990) "Phase Calibration of Imaging Radar Polarimetric Stokes Matrices",IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, pp. 246-252 Zebker H.A., J.J. van Zyl, and D.N. Held (1987) "Imaging Radar Polarimetry from Wave Synthesis",Journal Geophysical Research, Vol. 92, pp. 683-701 Zebker H.A., J.J. van Zyl, S.L. Durden, and L. Norikane (1991) "Calibrated Imaging RadarPolarimetry:Technique, Examples, and Applications", IEEE Transactions on Geoscience and RemoteSensing, Vol. GE-29, pp. 942-961

Page 2 of 2Bibliography - Polarimetry References

Radar Sea Ice References - Références radar liées aux glaces de mer

American Geophysical Union (1992) "Microwave Remote Sensing of Sea Ice". F.D. Carsey, ed.,American Geophysical Monograph 68, Washington, DC. Barber D.G. and E. LeDrew (1991) "SAR sea ice discrimination : A multivariate approach",Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 4, pp. 385-395 (CCRS #:1080012) Bertoia C., J. Falkingham, and F. Fetterer (1998) "Polar SAR Data for Operational Sea IceMapping", in Analysis of SAR Data of the Polar Oceans, C. Tsatsoulis and R. Kwok (Eds.), Berlin andHeidelberg: Springer-Verlag. ISBN 3-540-62802-9 Bjerkelund C.A., D.J. Lapp, R.O. Ramseier, and N.K. Sinha (1985) “The Texture and Fabric of theSecond Year Ice Cover at Mould Bay, Prince Patrick Island, NWT, April 1983”, InternationalGeoscience and Remote Sensing Symposium, IGARSS’85, Proceedings, Amherst, MA, 7-9 Oct.1985, pp. 426-431 (CCRS #: 1050457) Canadian Ice Service, Environment Canada, “Ice Codes and Symbols” http://www.cis.ec.gc.ca/about/code.html Canadian Ice Service, Environment Canada, “Ice Termininology” http://www.cis.ec.gc.ca/about/term.html Canadian Ice Service, Environment Canada, “Yearly Arctic Ice Atlas” http://www.cis.ec.gc.ca/ Carsey F.D. (1992) "Remote Sensing of Ice and Snow: Review and Status", International Journal ofRemote Sensing, Vol. 13, No. 13, pp. 5-11 Carsey F.D and R.K. Raney (eds.) (1989) "Special issue on the Labrador Ice Margin Experiment(LIMEX) and the Labrador Extreme Waves Experiment (LEWEX)", IEEE Transactions on Geoscienceand Remote Sensing, Vol. 27, No. 5 CCRS, Marine Applications, Case Study, “The Calving of Iceberg A-38, Ronne Ice Shelf, Antarctica" http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/marine/ice/calv00_e.html Ikeda M., C.E. Livingstone, and I. Peterson (1991) "A mesoscale ocean feature study usingsynthetic aperture radar imagery in the Labrador Ice Margin Experiment: 1989", Journal ofGeophysical Research, Vol. 96, No. C6, pp. 10,593-10,602 (CCRS #: 1081867) Jeffries M.O. and W.M. Sackinger (1990) "Ice island detection and characterization with airbornesynthetic aperture radar", Journal of Geophysical Research, Vol. 95, No. C4, pp. 5371-5377 Gray A.L. and L.D. Arsenault (1991) "Time-delayed reflections in L-band synthetic aperture radarimagery of icebergs", IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 2, pp.284-291 (CCRS #: 1078969) Kwok R., E. Rignot, B. Holt, and R. Onstott (1992) "Identification of Sea Ice Types in SpaceborneSynthetic Aperture Radar Data", Journal of Geophysical Research, Vol. 97, No. C2, pp. 2391-2402 Leconte R. and T.J. Pultz (1991) "Evaluation of the potential of RADARSAT for flood mapping using

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simulated satellite imagery", Canadian Journal of Remote Sensing, Vol. 17, No. 3, pp. 241-249(CCRS #: 1082471) Manore, M., “Ice Reconnaissance, Gulf of St Lawrence, Eastern Canada, March 6, 1996” http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/radarsat/images/que/rque01_e.html Molnia B.F. and J.E. Jones (1989) "View through ice: Are unusual airborne radar backscatterfeatures from the surface of the Malaspina Glacier, Alaska, expressions of subglacial morphology?",EOS, July 11, 1989, p. 701 and p. 710 Noetix Research Inc., Software Products, Auto Tracker http://www.noetix.on.ca/ Onstott R.G. (1992) "SAR and Scatterometer Signatures of Sea Ice, in Microwave Remote Sensingof Sea Ice", (F.D. Carsey ed.), Geophysical Monograph 68, American Geophysical Union,Washington, DC, pp. 73-104 Onstott R.G. and S.P. Gogineni (1985) "Active Microwave Measurements of Arctic Sea Ice underSummer Conditions", Journal of Geophysical Research, Vol. 90, No. C3, pp. 5035-5044 Picasso M., H. Salgado, and B. Lorenzo (1999) “Monitoreo de hielo marino”, Simposio FinalGlobeSAR 2, Buenos Aries, Argentina, 17-20 de Mayo 1999, pp. 103-108 Pultz T.J., R. Leconte, L. St. Laurent, and L. Peters (1991) "Flood mapping with airborne SARimagery: Case of the 1987 Saint John River Flood", Canadian Water Resources Journal, Vol. 16,No. 2, pp. 173-190 Ramsay B., M. Manore, L. Weir, K. Wilson, and D. Bradley (1998) "Use of RADARSAT Data in theCanadian Ice Service", Canadian Journal of Remote Sensing, Vol. 24, No. 1, pp. 36-42 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=3580 Rignot E. and M.R. Drinkwater, (1994). "Winter Sea-ice Mapping from Multi-parameter Synthetic-aperture Radar Data", Journal of Glaciology, Vol. 40, No. 134, pp. 31-45 Shuchman R.A, B.A. Burns, O.M. Johannessen, E.G. Josberger, W.J. Campbell, T.O. Manley, and N.Lannelongue (1987) "Remote sensing of the Fram Starit marginal ice zone", Science, Vol. 236, pp.429-431 (CCRS #: 1058473)

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Radar Tropical Environment References - Références radar liées aux environnements tropicaux

Ahern F.J., R.K. Raney, R.V. Dams, and D. Werle (1990) "A review of remote sensing for tropicalforest management to define possible RADARSAT contributions", Proceedings InterntionalSymposium on Primary Data Acquisition/In. Archives of Photogrammetric Engineering and RemoteSensing, Vol. 28, Part 1, pp. 141-157 (CCRS #: 1078404) Adams R.E.W., W.E. Brown, and T.P. Culbert (1981) "Radar mapping, archaeology, and ancientMaya land use", Science, Vol. 213, 25 Sept. 1981, pp. 1457-1463 Adeniyi P.O (1986) "A preliminary assessment of the probable impacts of the Lagos State (Nigeria)regional master plan (1980-2000)", Applied Geography, 1986, No. 6, pp. 223-240 Adeniyi P.O. (1984) "Land use and land cover in Nigeria: an appraisal of the Nigerian radarproject", The Nigerian Geographical Journal 27, Vol. 1 and 2 Aschbacher J. and J. Lichtenegger (1990) "Complementary nature of SAR and optical data: a casestudy in the tropics", ESA Earth Observation Quarterly, No. 31, September 1990, pp. 4-8 Banyard S.G. (1979) "Radar: Interpretation based on photo-truth keys", ITC Journal, 1979-2, pp.267-276 Cimino, J.B. and C. Elachi (1982) "Shuttle Imaging Radar - A (SIR-A) experiment", JPL Publ. 82-77, Jet Propulsion Laboratory, Pasadena, CA (CCRS #: 1055998) Curlander J. and R.N. McDonough (1991) "Synthetic Aperture Radar - systems and signalprocessing", J. Wiley & Sons, New York, 647 p. Dams R.V., D. Flett, M.D. Thompson, and M. Lieberman (1987) "SAR image analysis for CostaRican tropical forestry applications", SELPER/II Simposio Latino Americano Sobre SensoresRemotos, Bogota, Colombia, pp. 22-28 de Molina I. and C. Molina (1989) "The use of high resolution radar imagery in forest inventories intropical forests of Cativo (prioria copaifera)", Proc. 1989 SELPER Conference, Buenos Aires,Argentina, 11 p. Eden M.J. and J.T. Perry (eds.) (1986) "Remote sensing and tropical land management", JohnWiley, London, New York, Sydney, Toronto Elachi C. (1988) "Spaceborne radar remote sensing: applications and techniques", IEEE Press, NewYork, NY, 255 p. Fagbami A. and A. Fapohunda (1986) "SLAR imagery for soil mapping and regional planning inwestern Nigeria", Chapter 4, pp. 55-77, in: Eden, M.J. & J.T. Perry (eds.) (1986) Remote sensingand tropical land management, John Wiley, London, New York, Sydney, Toronto Ford J.P. and D.J. Casey (1988) "Shuttle radar mapping with diverse incidence angles in therainforest of Borneo", International Journal of Remote Sensing, Vol. 9, No. 5, pp. 927-943 Furley P.A. (1986) "Radar surveys for resource evaluation in Brazil: an illustration from Rondonia",pp. 79-99 In: Eden, M.J. & J.T. Perry (eds.), Remote sensing and tropical land management, JohnWiley, London, New York, Sydney, Toronto

Page 1 of 3Bibliography - Radar Tropical Environment References

Gaddis L., P. Mouganis-Mark, R. Singer, and V. Kaupp (1989) "Geologic Analyses of ShuttleImaging Radar (SIR-B) data of Kilauea Volcanoe, Hawaii," Geol. Soc. Amer. Bull., Vol. 101, pp.317-323 GlobeSAR-2, Simposio Final, Proceedings, "Aplicaciones de RADARSAT en América Latina", BuenosAires, Argentina, 17-20 de Mayo, 1999 http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/arg/gs2arpubl_e.html Harris J., R. Murray, and T.K. Hirose (1990) "The IHS transform for the integration of radar datawith other remotely sensed data", Journal of Photogrammetric Engineering and Remote Sensing,Vol. 56, No. 12, pp. 1631-1641 (CCRS #: 1078504) Hess L.A., J.M. Melack, and D.S. Simonett (1990) "Radar detection of flooding beneath the forestcanopy: A review", International Journal of Remote Sensing, Vol. 11, No. 7, pp. 1313-1325 (CCRS#: 1076725) Imhoff M.L. and D.G. Gesch (1990). "The derivation of a sub-canopy digital terrain model of aflooded forest using synthetic aperture radar", Photogrammetric Engineering and Remote Sensing,Vol. 56, No. 8, pp. 1155-1162 Imhoff M.L., C. Vermillion, M.H. Story, A.M. Choudhury, A. Gafoor, and F. Polcyn (1987) "Monsoonflood boundary delineation and damage assessment using spaceborne imaging radar and Landsatdata", Photogrammetric Engineering and Remote Sensing, Vol. 54, No. 4, pp. 405-413 Imhoff M.L., C.H. Story, C.H. Vermillion, F. Khan, and F. Polcyn (1986) "Forest canopycharacterization and vegetation penetration assessment with spaceborne radar", IEEE Transactionson Geoscience and Remote Sensing, Vol. GE-24, No. 4, pp. 535-541 Koopmans B.N. (1986) "Satellite radar interpretation of the Bintuni Basin area, Eastern VogelkopPeninsula, West Irian, Indonesia", Geologie en Mijnbouw, Vol. 65, pp. 197-204 Koopmans B.N. (1983) "Side-looking radar - A tool for geological surveys", Remote SensingReviews, Vol. 1, pp. 19-69 Krohn M.D., N.M. Milton, and D.B. Segal (1983) "SEASAT SAR response to lowland vegetationtypes in eastern Maryland and Virginia", Journal of Geophysical Research, Vol. 88, No. C3, pp.1937-1952 Leberl F.W. (1990) "Radargrammetric image processing", 595 p. Artech House, Norwood, MA(CCRS #: 1074081-1074101) Lewis A.J. (1977) "Coastal mapping with radar", Geoscience and Man, Vol. 18, pp. 239-247 Lind A. (1984) "An analysis of spaceborne imaging radar - Songkhla Barrier, South Thailand",Photointerpretation, 84-5/2, 3p. MacDonald, H.C., A.J. Lewis and R.S. Wing (1971) "Mapping and landform analysis of coastalregions with radar", Geol. Soc. Amer. Bull., Vol. 82, pp. 345-358 MacDonald H.C. (1969) "Geologic evaluation of radar imagery from Dairen Province, Panama",Modern Geology, Vol. 1, pp. 1-63 Mercer J.B. and M.E. Kirby (1987) "Topographic mapping using STAR-1 radar data", Geocarto


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International, 1987, No. 3, pp. 39-42 (CCRS #: 1062572) Muenlek S.T. and B.N. Koopmans (1983). "The Shuttle Imaging Radar over south peninsulaThailand", ITC Journal, 1983-3, pp. 258-269 Ormsby J., B. Blanchard, and A. Blanchard (1985). "Detection of lowland flooding using activemicrowave systems", Photogrammetric Engineering and Remote Sensing, Vol. 51, No. 3, pp. 317-328 Parry D.E. and J.W. Trevett (1979). "Mapping Nigeria's vegetation from radar", GeographicalJournal 145, No. 2, pp. 265-281 Pope K.O. (1988). "Radar remote sensing of seasonal inundation in the Bajos near El Mirador,Peten, Guatemala, in: Dahlin, B.H. (ed.) Human Ecological studies at El Mirador", New WorldArchaeological Foundation Publications, Provo Raney R.K., F.J. Ahern, R.V. Dams, and D. Werle (1990). "A review of radar remote sensing fortropical forest management", UN/FAO/ESA Microwave Workshop, INPE, Brazil, 19-23 Nov. 1990,20p. Rebillard P. and T. Dixon (1984). "Geologic interpretation of SEASAT SAR imagery near the RioLacuntum, Mexico", in Teleki, P. & V. Weber (eds.) Remote sensing for geological mapping,Proceedings IUGS/UNESCO Seminar, Orleans, USA, IUGS Publ. No. 18, pp. 129-142 Ringrose S.M. and P. Large (1983). "The comparative value of LANDSAT print and digitized dataand radar imagery for ecological land classification in the humid tropics", Canadian Journal ofRemote Sensing, Vol. 9, No. 1, pp. 45-60 Sabins F.F. (1983). "Geologic interpretation of Space Shuttle radar images of Indonesia", Amer.Assoc. Petrol. Geol. Bull., Vol. 67, No. 11, pp. 2076-2099 Sader S.A, T.A. Stone, and A.T. Joyce (1990). "Remote sensing of tropical forests: An overview ofresearch and applications using non-photographic sensors", Photogrammetric Engineering andRemote Sensing, Vol. 56, No. 10, pp.1343-1351 Sicco-Smit (1978) "SLAR for forest type classification in a semi-deciduous tropical region", ITCJournal, 1978-3, pp. 385-401 Southworth C.S. (1984) "Structural and hydrogeologic applications of remote sensing data, easternYucatan Peninsula, Mexico", In: Beck, B. (1984) Sinkholes: Their geology, engineering andenvironmental impact, Proceedings 1st Multi-disciplinary Conference on Sinkholes, Orlando, FL, pp.59-64 Stone, T.A. and G.M. Woodwell (1988). "SIR-A analysis of land use in Amazonia", InternationalJournal of Remote Sensing, Vol. 9, No. 1, pp. 95-105 Trevett J.W. (1986) "Imaging radar for resource surveys", London, New York Wadge G. and T.H. Dixon (1984) "A geological interpretation of SEASAT SAR imagery of Jamaica",Journal of Geology, Vol. 92, pp. 561-581


Radar Remote Sensing Textbooks - Livres de télédétection radar

Curlander J.C. and R.N. McDonough (1991) "Synthetic Aperture Radar Systems and SignalProcessing," John Wiley and Sons, Inc., Toronto Carrara W.G., R.S. Goodman and R.M. Majewski (1995) "Spotlight Synthetic Aperture Radar SignalProcessing Algorithms", Artech House, Boston, 1995 Elachi C. and F.T. Ulaby (1990) "Radar Polarimetry for Geoscience Applications", Artech House,Boston Elachi C. (1988) "Spaceborne Radar Remote Sensing: Applications and Techniques", IEEE Press,New York Fitch J.P. (1988) "Synthetic Aperture Radar", Springer-Verlag, New York Henderson F.M. and A.J. Lewis, Eds. (1998) "Principles and Applications of Imaging Radar", Manualof Remote Sensing, Third Edition, Volume 2, John Wiley & Sons, Inc., Toronto Oliver C. and S. Quegan (1998) "Understanding Synthetic Aperture Radar Images", Artech House,Norwood, Mass. Ulaby F.T. and M.C. Dobson (1989) "Handbook of Radar Scattering Statistics for Terrain", ArtechHouse, Norwood, Mass. Ulaby F.T., Moore, R.K. and Fung, A.K. (1986) "From Theory to Applications", Vol. III, MicrowaveRemote Sensing: Active and Passive, Artech House, Inc., Norwood, MA Ulaby F.T., R.K. Moore and A.K. Fung (1982) "Radar Remote Sensing and Surface Scattering andEmission Theory", Vol. II, Microwave Remote Sensing, Active and Passive, Addison-WesleyPublishing Company, Reading, MA, 1069 pages Ulaby F.T., R.K. Moore and A.K. Fung (1981) "Microwave Remote Sensing Fundamentals andRadiometry", Vol. I, Microwave Remote Sensing: Active and Passive, Addison-Wesley PublishingCo., Reading, MA., 456 pages

Page 1 of 1Bibliography - Radar Remote Sensing Textbooks

Intermediate Radar References - Références radar Intermédiaire

Brown R.J., B. Brisco, R. Leconte, D.J. Major, J.A. Fischer, G. Reichert, K.C. Korporal, M. Bullock,H.T. Pokrant, and J. Culley (1993) "Potential Applications of Radarsat data to Agriculture andHydrology", Canadian Journal of Remote Sensing, Vol. 19, No. 4, Nov-Dec. 1993, pp. 317-329(CCRS #: 109536) DeSève D., Th. Toutin et R. Desjardins (1996) "Évaluation de deux méthodes de correctionsgéométriques d'images Landsat-TM et ERS-1 RAS dans une étude de linéaments géologiques",International Journal of Remote Sensing, Vol. 17, No. 1, pp. 131-142 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=1622 Drury S.A. (1993) "Image Interpretation in Geology", Second Edition, Chapman and Hall, pp. 135 ESRI, Using Grid in Arc/Info, Training Manual, ESRI Evans D.L. and M. Moghaddam (Editors) (1998) LightSAR Science Requirements and MissionEnhancements, NASA et JPL, 24 p. Fullerton J.K., F. Leberl, and R.E. Marque (1986) "Opposite-side SAR Image Processing For StereoViewing", Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 9, September 1986, pp.1487-1498 Henderson F.M and A.J. Lewis (ed.) (1998) "Principles and Applications of Imaging Radar", Manualof Remote Sensing, Third Edition, Volume 2, ASPRS, John Wiley and Sons Inc., Toronto Jensen, J. R. (1996) "Introductory Digital Image Processing: A Remote Sensing Perspective", 2ndedition, Prentice-Hall, Inc., 231 p. Jordan R.L., B.L. Huneycutt and M. Werner (1995) "The SIR-C/X-SAR Synthetic Aperture RadarSystem", IEEE Trans. on Geoscience and Remote Sensing, Vol. 33, No. 4, pp. 829-839 Lopes A., E. Nezry, R. Touzi, and H. Laur (1993) "Structure Detection and Statistical AdaptiveSpeckle Filtering in SAR Images", International Journal of Remote Sensing, Vol. 14, No. 9, pp.1735-1758 Lopes A., R. Touzi, and E. Nezry (1990) "Adaptive speckle filters and scene heterogeneity", IEEETrans. on Geocience and Remote Sensing, Vol. 28, No. 6, Nov. 1990 Lukowski T.I., R.K. Hawkins, K.P. Murnaghan, and S.K. Srivastava (1998) "RADARSAT AntennaElevation Gain Pattern Determination", Canadian Journal of Remote Sensing, September 1998, Vol.24, No. 3, pp. 286-291 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=3595 Lukowski T.I., R.K. Hawkins, C. Cloutier, J. Wolfe, L.D. Teany, S.K. Srivastava, B. Banik, R. Jha,and M. Adamovic (1997) "RADARSAT Antenna Pattern Determination", Proceedings, Geomatics inthe Era of RADARSAT (GER'97), Ottawa, Canada, 27-29 May 1997, pp. 6 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=3137 Luscombe T. (1997) "Nadir Ambiguities in RADARSAT Imaging", Proceedings of a Workshop onRADARSAT Data Quality, CEOS Working Group on Calibration and Validcation, SAR Calibration Sub-group, Ottawa, Canada, 27-29 May 1997

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PCI Gematics Inc. (1997 and 1993) "PCI User Manuel" RADARSAT International, (2000) "RADARSAT Data Products Specificiations", RSI-GS 026,Version3/0, May 8, 2000 http://www.rsi.ca/adro/adro/tools/tools/cdpf_specs/d4_3-0.doc RADARSAT International (1995) "RADARSAT Illuminated - Your Guide to Products and Services" Raney R.K. (1998) "Radar Fundamentals: Technical Perspective", Chapter 2 in Principles andApplications of Imaging Radar, Manual of Remote Sensing, Third Edition, Volume 2, ASPRS, JohnWiley and Sons Inc., Toronto Raney R.K. (1991) "Considerations for SAR Image Quantification Unique to Orbital Systems", IEEETransactions on Geoscience and Remote Sensing, September 1991, Vol. 29, No. 5, pp. 754-760(CCRS #: 1083285) Sardar A.M. (1997) "The Evolution of Space-Borne Imaging Radar Systems: A ChronologicalHistory", Canadian Journal of Remote Sensing, Vol. 23, No. 3, pp. 276-280 Schowengerdt, Robert A. (1983) "Techniques for Image Processing and Classification in RemoteSensing", Academic Press, New York Shepherd N., ALTRIX Systems (2000) "Extraction of Beta-Nought and Sigma-Nought fromRADARSAT CDPF Products", Canadian Space Agency Document AS97-5001, Rev. 4, 28 April 2000 http://www.space.gc.ca/csa_sectors/earth_environment/radarsat/radarsat_info/description/radio_calib.a Srivastava S.K., T.I. Lukowski, and C. Cloutier (1997) "Calibration and Image Quality PerformanceResults of RADARSAT", Advances in Space Research, Vol. 19, No. 9, pp. 1447-1454 Toutin Th. (1999) "Error Tracking of Radargrammetric DEM from RADARSAT Images", IEEETransactions on Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2227-2238 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/3604.pdf Toutin Th. and B. Rivard (1997) "Value-Added RADARSAT Products for Geoscientific Applications",Canadian Journal of Remote Sensing, Vol. 23, Nol. 1, pp. 63-70, 1997 http://dweb.ccrs.nrcan.gc.ca/ccrs/db/biblio/paper_e.cfm?BiblioID=2206 Toutin Th. (1995) "Multisource Data Fusion with an Integrated and Unified Geometric Modelling",EARSeL Advances in Remote Sensing, Vol. 4, No. 2, pp. 118-129 http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/1223.pdf Toutin, Th. and Y. Carbonneau (1992) “MOS and SEASAT Image Geometric Correction”, IEEE-Transactions on Geoscience and Remote Sensing, Vol. 30, No. 3, pp. 603-609 (CCRS #: 1088108) Touzi R. (1999) "A Protocol for Speckle Filtering of SAR Images", Proceedings of CEOS Workshop,Toulouse, France, October , 1999 , 6 p. http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4717.pdf Touzi R., A. Lopes, and P. Bousquet (1998) "A statistical and geometrical edge detector for SARimage", IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 6, pp. 764-773 Ulaby F.T. and M.C. Dobson (1989) "Handbook of Radar Scattering Statistics for Terrain",University of Michigan, Radiation Lab.


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Werle D. (1997) "An Occurrence of RADARSAT SAR Azimuth Ambiguity Patterns - Observations inHalifax Harbour and Implications for Applications Work", Proceedings, Geomatics in the Era ofRADARSAT (GER'97), Ottawa, Canada, 27-29 May 1997


Radar Glossary

a | b | c | d | e | f | g | h | i | j | k | l | m | n | o | p | q | r | s | t | u | v | w | x | y | z Source: Raney, R. Keith (Principal Professional Staff), 1999, Radar Glossary, Johns Hopkins University, Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723-6099.

ABSORPTION

Reduction in strength of an electromagnetic wave propagating through a medium. The absorption of an electromagnetic wave is determined by the dielectric properties of the material.

ACTIVE RADAR CALIBRATOR (ARC)

Ground-based microwave device that generates in the final image a point response of known strength (radar cross section) and position. When illuminated by a pulse from a SAR, an ARC amplifies it and retransmits the pulse back towards the radar. An ARC may impose controlled amounts of time delay, Doppler shift, or polarization rotation onto the returned signal to aid in specific calibration objectives.

ALMAZ

A Russian SAR satellite launched in May 1991, which operated until October 1992.

ALONG-TRACK

Dimension parallel to the path of the vehicle carrying the radar. For side-looking radars, this dimension is sometimes called the cross range or azimuth direction.

ALOS

Advanced Land Observing Satellite, planned to be launched by NASDA, Japan, in 2002. The payload will include the PALSAR imaging radar system.

AMPLITUDE

Measure of the strength of a signal, and in particular the strength or "height" of an electromagnetic wave (units of voltage). The amplitude may imply a complex signal, including both magnitude and the phase.

ANTENNA

Device to radiate electromagnetic energy on transmission by a radar, and to collect such energy during reception. An antenna pattern is designed with spatial directivity, which concentrates the energy into a beam in both the vertical (elevation) and the horizontal (azimuth) directions. The electrical losses of an antenna together with its directivity determine the antenna gain. In general, the beamwidth in any plane is inversely proportional to the aperture width in that plane, and directly proportional to the wavelength of the radiation. Polarization on transmit and on receive is determined by the antenna.

ASAR

Advanced Synthetic Aperture Radar to be included in 2001 in the payload of ESA’s Envisat. ASAR, a C-band SAR, will have variable incident angle, variable swath widths and resolutions, and will include a ScanSAR mode. Further, it will introduce the Global Monitoring Mode, a 1 km resolution 405 km swath width mode with either HH or VV polarization. Dual polarizations will be available, but not full quadrature polarimetry.

Page 1 of 19Glossary

ASPECT ANGLE

Description of the geometric orientation in the horizontal plane of an object in the scene with respect to the illuminating wavefront. (See incident angle.)

ATTENUATION

Decrease in the strength of a signal. The decrease in the strength of a signal is usually described by a multiplicative factor in the mathematical description of signal level. A signal is attenuated by application of a gain less than unity. Common causes of attenuation of an electromagnetic wave include losses through absorption and by volume scattering in a medium as a wave passes through.

AUTOMATIC GAIN CONTROL

Adaptive change in radar gain in the along-track direction, to compensate for changes in average scene reflectivity.

AZIMUTH

The relative along-track position of an object within the field of view of an antenna following the moving radar's line of flight. The term is commonly used to indicate linear distance or image scale in the along-track direction.

AZIMUTH AMBIGUITIES

(See DOPPLER shift).

AZIMUTH RESOLUTION

Resolution characteristic of the azimuth dimension, usually applied to the image domain. Azimuth resolution is fundamentally limited by the Doppler bandwidth of the system. Excess Doppler bandwidth is usually used to allow extra looks, at the expense of azimuth resolution.

BACKSCAN MODE

Special mode that is a logical compromise between the SpotSAR mode and the strip-map mode, which implies that the azimuth Doppler bandwidth, and hence the achievable resolution, are enhanced.

BACKSCATTER

The (microwave) signal reflected by elements of an illuminated scene back in the direction of the radar. It is so named to make clear the difference between energy scattered in arbitrary directions, and that which returns to the radar and thus may be received and recorded by the sensor.

BANDWIDTH

A measure of the span of frequencies available in a signal or other distribution, or of the frequency limiting stages in the system. For a SAR system, typical bandwidths in the range channel are on the order of 20 Megahertz, and in the azimuth channel are on the order of 1 Kilohertz. Bandwidth is a fundamental parameter of any imaging system, and determines the ultimate resolution available. For any pulse, the basic parameter that describes its structure is the time bandwidth product.

BEAM VELOCITY

Rate of progress along the imaged surface of the antenna’s illumination pattern. In the case of a satellite SAR’s strip-map mode, the beam velocity (alternatively the footprint velocity) is smaller than the satellite velocity by the


ratio of the footprint’s radius of rotation to the satellite’s orbit radius (with respect to the Earth’s centroid). The sub-satellite point on the earth’s surface has velocity about 6.6 km/s for typical SAR satellites. Since the imaged area is laterally offset, and therefore closer to the axis of rotation of the satellite, the beam velocity will be smaller.

BEAMWIDTH

A measure of the radiation pattern of an antenna. For SAR applications, both the vertical beamwidth and the horizontal beamwidth or azimuthal antenna pattern are frequently used concepts.The vertical beamwidth affects the width of the illuminated swath. The horizontal beamwidth determines, indirectly, the azimuth resolution. Beamwidth may be measured in the one-way or two-way form, and in either voltage or power.

BETA NOUGHT (β°)

Radar brightness coefficient. The reflectivity per unit area in slant range, dimensionless. (See sigma nought).

BRAGG SCATTERING

Enhanced backscatter due to coherent combination of signals reflected from a rough surface having features, with periodic distribution in the direction of wave propagation, and whose spacing is equal to half of the wavelength as projected onto the surface.

BRIGHTNESS

Property of a radar image (digital or optical) in which the observed strength of the radar reflectivity is expressed as being proportional to a digital number (digital image file) or to a grey scale mapping, which, for a photographic positive, shows "bright" as "white".

C-BAND

Microwave band in which the wavelengths are at or near 5.6 cm.

CALIBRATION

The act or process of comparing certain specific measurements in an instrument with a standard.

CANADA CENTER FOR REMOTE SENSING (CCRS)

The leading centre in Canada for the development of imaging radar and other remote sensing applications and technology.

CANADIAN SPACE AGENCY (CSA)

Organisation located in St. Hubert, Québec, Canada.

CHIRP

Typical phase coding or modulation applied to the range pulse of an imaging radar designed to achieve a large time-bandwidth product. The resulting phase is quadratic in time, which has a linear derivative such that coding is often called linear frequency modulation, or linear FM.

CHIRP SCALING

SAR processing algorithm that corrects for range curvature and two-dimensional focusing with no interpolation.


COHERENT

Property of a signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. The combined power Pcoh of a set of coherent signals {si} is the vector sum of the signals, magnitude squared, Pcoh = s1 + s2 + …2 (See incoherent.)

COHERENT REFLECTOR

Simple or complex surface (such as a corner reflector) from which reflected wave components are coherent with respect to each other, and thus combine to yield larger effective power than would be observed from a diffuse scattering surface of the same area.

COMPLEX (NUMBER)

For radar systems, this implies that the representation of a signal or data file needs both magnitude and phase measures. In the digital SAR context, a complex number is often represented by an equivalent pair of numbers, the in-phase (I ) component and the quadrature (Q) component. For any complex number a, the relationships are

a = re jϕ= I + jQ, where I = r cosϕ, Q = r sinϕ, and j = (-1)1/2. In the exponential notation, r is the magnitude and ϕ is the phase of the number a, which is the complex amplitude (sometimes simply called "amplitude" which could be confused with "magnitude"). For coherent systems such as SAR, the role of complex numbers is an essential part of the signal, since signal phase is used in the processor to obtain high resolution.

CONDUCTIVITY

Property of a material to allow electrical current to flow with very little loss. For natural surfaces, conductivity in general is increased with increased moisture content.

CONSERVATION OF CONFUSION

Principle, for imagery derived from a given SAR, that the amount of "information" in the data is constant. One expression of this rule is that the product of the range and the azimuth resolution divided by the number of statistically independent looks is a constant. This constant serves as a figure of merit for the number of looks of the system, a measure of SAR performance. (In this context, "information" is related to the statistical degrees of freedom in the data ensemble, and not necessarily to knowledge about objects in the scene.) Two special consequences of this principle: 1) the minimum impulse response width is the system (ideal) resolution, and 2) there is a trade-off between resolution and speckle reduction.

CONSERVATION OF COORDINATES

Principle, for synthetic aperture radar imagery. The position of a coordinate in an image is theoretically not changed by pitch, roll, or yaw rotations of the radar. Range is determined by the speed of light, and azimuth is determined by the along-track radar velocity.

CONSERVATION OF ENERGY

Principle of SAR. Assuming that all available data is used for each case, then the average value of the estimated reflectivity from a scene is a constant for a given SAR and processor. The value is independent of the number of looks used, and independent of any time varying noncoherence in the scene (such as from a moving surface of water) or in the radar/processor combination.

CONTRAST

Difference between the tone of two neighbouring regions.


CORNER REFLECTOR

Combination of two or more intersecting specular surfaces that combine to enhance the signal reflected back in the direction of the radar. Strongest reflection is obtained when the materials are good conductors.

DECIBEL (dB)

Measurement of signal strength, properly applied to a ratio of powers: a signal power P compared, by ratioing, to a reference power Pref. The formal definition of the power ratio in the decibel scale is PdB = 10 log

10 (P / Pref ). For

example, the power ratio of 1/2 corresponds to "-3 dB", derived from log10

(0.5) = -0.3010. Decibels often are used in radar, such as in measures of reflectivity, for which the dynamic range may span several factors of ten. The unit is named in honour of Alexander Graham Bell, inventor of the telephone.

DEGREE OF POLARIZATION

Ratio of the power in the polarized part of an electromagnetic wave to the total power; P = (s12 + s

22 + s

32)1/2 / s

0 in

terms of the Stokes parameters.

DEPRESSION ANGLE

Usually refers to the line of sight from the radar to an illuminated object as measured from the horizontal plane at the radar. For image interpretation, use of the term is not recommended because it does not account for the effects of Earth curvature, and it does not conveniently include effects of local slope in the scene. It is more appropriate for an engineering description of the vertical antenna pattern at the radar itself. (See incident angle.)

DETECTION (Radar)

Processing stage at which the strength of the signal is determined for each pixel value. Detection removes phase information from the data file. The preferred detection scheme uses a magnitude squared method, s2, which is energy conserving, and has units of voltage squared per pixel.

DIELECTRIC

Material which has neither "perfect" conductivity nor is perfectly "transparent" to electromagnetic radiation. The electrical properties of all intermediate materials, such as ice, natural foliage, or rocks, may be described by two quantities: relative dielectric constant and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into the material are determined by these two quantities.

DIELECTRIC CONSTANT

Fundamental (complex) parameter, also known as the complex permittivity, that describes the electrical properties of a lossy medium, e.g., a target which has attenuation. (See permeability.) By convention, the relative dielectric constant of a given material is used, defined as the (absolute) dielectric constant divided by the dielectric constant of "free space". The (relative) dielectric constant is usually defined as ε = ε’ - jε” (It is common practice to refer to the real component ε’ as "the dielectric constant", whose partner, the loss tangent, accounts for ε”.)

DIFFUSE

Reflection typically made up of many individual reflections having random phase with respect to each other, such as from a natural forest canopy or agricultural field. The term is also used to describe a surface that reflects (microwave) illumination in this fashion. (The opposite term is specular or coherent.)


DIGITAL NUMBER (DN)

A number, between zero and 255 for example, assigned to each spatial grid position in the file representing the brightness levels of an image. The digital numbers may be related to sigma nought of scene elements through the process of calibration.

DIHEDRAL

Corner reflector formed by two surfaces orthogonally (perpendicularly) intersecting. For enhanced backscatter, the dihedral must be open to the radar, and have the axis of intersection at right angles to the direction of illumination.

DISTRIBUTED SCATTERERS

Elements of a scene consisting of many small scatterers of random location, phase, and reflectivity in each resolution cell. (See diffuse.)

DISTRIBUTION OF RADAR SIGNAL

General purpose mathematical description of a signal characterized by values with magnitude significantly larger than zero over only a relatively small span in time or distance. A distribution may have extensive low level tails or sidelobes. Examples of distributions include the pulse transmitted by a radar, and the description in space of the pattern of an antenna.

DOPPLER BANDWIDTH

Doppler frequency is the (time) derivative of the phase history generated by a coherent radar as it passes an illuminated scatterer. Doppler bandwidth is a measure of the spread in Doppler frequencies over the phase history. The reciprocal of Doppler bandwidth is equal to the available azimuth resolution (in seconds), which usually is converted to spatial azimuth resolution through multiplication by the beam velocity.

DOPPLER SHIFT

The apparent change of frequency of sound waves or electromagnetic waves, varying with the relative velocity of the source and the observer. Shift in frequency caused by relative motion along the line of sight between the sensor and the observed scene. In SAR, it is more formally the first derivative of the signal phase in the azimuth direction. The distance between the highest and lowest Doppler frequnecies must be smaller than the azimuth pulse repetition frequency (PRF). If the difference is larger, false image features (azimuth ambiguities) will occur in the images.

DYNAMIC RANGE

A description of the variety of signal amplitudes (or power levels) available in a system, or present in a data file. Dynamic range is specified either i) to be within minimum and maximum values, or ii) with respect to the ratio of maximum to minimum values. The most important specification is linear dynamic range over which signals combine according to the property of linearity.

ELECTROMAGNETIC (EM) WAVE

A wave described by variations in electric and magnetic fields, elegantly formulated by J. C. Maxwell in 1873. Light waves, radio waves, and microwaves are well known examples of electromagnetic waves. All such waves propagate at the speed of light in "free space", which includes most realistic atmospheric conditions. Three material parameters are necessary and sufficient to describe EM waves in a given medium: dielectric constant (or permittivity); permeability; and conductivity.


ELEVATION DISPLACEMENT

Image distortion in the range direction of a side and downward looking radar caused by terrain features in the scene being above (or below) the reference elevation contour. As a result, the position of these features in the image is closer to (or further from) the radar than their planimetric position. The effect may be used to create radar stereo images (see parallax). It may be removed from an image through independent knowledge of the terrain profile. In many applications, an approximate correction may be derived from the shapes of displaced features using shading techniques.

ENERGY (RADAR WAVE)

For a waveform of time-limited duration such as a radar pulse reflected by an object, the pulse energy is given by the power of the signal integrated over the duration of the signal (Units of watt-seconds = joules).

ENVISAT

The Environmental Satellite, a very large (8000 Kg, 10 m x 4 m x 4 m, launch configuration) multi-sensor earth-observing satellite from ESA, is scheduled for launch in mid-2001. The payload includes an imaging radar (ASAR), and a radar altimeter. Unlike its predecessors ERS-1 and ERS-2, there is no scatterometer aboard.

EOS SAR (EARTH OBSERVING SYSTEM SATELLITE SYNTHETIC APERTURE RADAR)

A proposal by JPL for a three frequency quadrature polarimetric SAR for the Earth Observation Satellite series.

ERS-1

Satellite launched by ESA in July 1991. The main payload of ERS-1, the AMI instrument, includes a SAR at C-band, VV polarization and 23° incident angle. Other major instruments: the Radar Altimeter (RA), the Along-Track Scanning Radiometer (ATSR), the Microwave Radiometer (MWR) and the Precise Range and Range Rate Experiment (PRARE).

ERS-2

Launched in 1995, this ESA satellite is very similar to ERS-1. During the tandem mission, ERS-1 and ERS-2 passes were separated by only one day. The objective of the mission was to gather data for interferometric studies. The two satellites were controlled in synchronized orbits for about one year

EUROPEAN SPACE AGENCY (ESA)

An international organization dedicated to space research and development. Their mission is to advance the peaceful application of space technology in Europe.

FORESHORTENING

Spatial distortion which occurs where terrain slopes are facing a side-looking radar's illumination. The distance between the slope and the radar is smaller relative to what it would be if the same terrain was level, so the sloping terrain appears compressed in range scale of the image. Foreshortening is a special case of elevation displacement. The effect is more pronounced for steeper slopes, and for radars that use steeper look angles. Range scale expansion, the complementary effect, occurs for slopes that face away from the radar illumination.

FOURIER TRANSFORM

Mathematical operation used to derive the frequency domain description of a distribution. An efficient digital implementation is the "fast Fourier transform", or FFT. The inverse Fourier transform returns a frequency domain description to the original distribution. The digital inverse form is known as the IFFT.


FREQUENCY

Rate of oscillation of a wave. In the microwave region, frequencies are on the order of 1 GHz (Gigahertz) to 100 GHz. ("Giga" implies multiplication by a factor of a billion.) For electromagnetic waves, the product of wavelength and frequency is equal to the speed of propagation, which, in free space, is the speed of light.

FREQUENCY DOMAIN

For every distribution f in time there is an equivalent representation F whose independent variable is frequency. The frequency domain representation is the Fourier transform of the original distribution. F and f are equivalent in the sense that they carry the same information, but it is expressed in an alternative way. The concept is often generalized to distributions in the space domain. The Fourier transform is then in the spatial frequency domain and has units of cycles per unit length. The azimuth frequency domain is also known as the Doppler domain.

GAIN

Change in signal level due to processing functions that increase the magnitude of the signal. Examples include: signal amplification in a radar receiver; processing gain in the processor; and antenna gain, a result of the directivity of the pattern.

GAUSSIAN

The classical distribution characterized by a "bell-shaped" curve. This normal distribution plays several roles in SAR. For example, it is the "normal" probability distribution that describes the in-phase and the quadrature components of the signal corresponding to a surface that produces diffuse scattering. Targets which exhibit this distribution are sometimes described as Gaussian scatterers.

GRAZING ANGLE

Angle between the mean horizontal at the scene and the incoming radar illumination. The concept is most apt for ship-borne or aircraft radars when the illumination is itself close to horizontal.

GROUND RANGE

Range direction of a side-looking radar image as projected onto the nominally horizontal reference plane, similar to the spatial display of conventional maps. For spacecraft data, an Earth geoid model is used, whereas for airborne radar data, a planar approximation is sufficient. Ground range projection requires a geometric transformation from slant range to ground range, leading to relief or elevation displacement, foreshortening, and layover unless terrain elevation information is used.

HERTZ

Named after H. R. Hertz, a 19th century German physicist, it is the standard unit for frequency, equivalent to one cycle per second.

HISTOGRAM

Graph which plots the number of samples versus the digital number (the statistical distribution of brightness) of data selected from a region of an image.

IMAGE (RADAR)

Mapping of the observed radar reflectivity of a scene. For radars with digital image processing, the image consists of a file of digital numbers assigned to spatial positions on a grid of pixels, and presented either as hard copy (such as a photographic print) or soft copy (such as a digital data record). All radar images are subject to statistical variations, mainly speckle and noise. These variations must be accommodated in either visual or


numerical image interpretation. The most commonly used image formats occur after detection. After calibration (and compensation for speckle and noise effects), image files from magnitude squared detection are proportional, on average, to sigma nought σ0. Magnitude scaling (formed by taking the square root of the power image which is proportional to σ0) is the "standard" for most SAR image files. A magnitude image often yields a photographic copy that is more readily interpreted visually, and requires less dynamic range and data storage space. A digital SAR image file may be retained in complex format (before detection) for specialized applications.

IMAGE QUALITY (SAR)

QSAR, equal to the product of the number of (statistically independent) looks in range and in azimuth, divided by the product of the range and the azimuth resolutions. This parameter is proportional to the product of the range and azimuth bandwidths, and thus is a fundamental estimate of the end-to-end information capacity of the system.

IMPULSE RESPONSE

Also known as the point spread function, it is the two-dimensional brightness pattern in an image (after processing) corresponding to the signal reflected by an object whose sigma falls within the dynamic range of the system, and for which the width of the imaged pattern is determined by the radar and processor rather than by the size of the object. (A trihedral corner reflector is the most commonly used object for generating an impulse response in a test image.) A "good" impulse response has a relatively large value for the pixel that maps the point scatterer location, and very small values for all surrounding pixels. The impulse response is a basic building block in describing a given radar's imaging performance, since an image is built up from the linear combination of impulse responses from all individual scatterers illuminated by the radar. The impulse response width (IRW, or resolution) of the central peak is the most important characteristic of the impulse response, together with the shape of the impulse response distribution both close to and remote from its centre.

IN-PHASE ( I )

Component of the signal that has the same phase as the complex reference frequency. In-phase is represented by the constant I.

INCIDENT ANGLE

Angle between the line of sight from the radar to an element of an imaged scene, and a vertical direction characteristic of the scene. The definition of "vertical" for this purpose is important. One must distinguish between the (nominal) "incident angle" determined by the large scale geometry of the radar and the Earth's geoidal surface, and the local incident angle which takes into account the mean slope with each pixel of the image. Smaller incident angle refers to viewing line of sight being closer to the (local) vertical, hence "steeper". (See aspect angle.) In general, reflectivity from distributed scatterers decreases with increasing incident angle.

INCOHERENT (OR NONCOHERENT)

Property of a signal or data set in which the phases of the constituents are not statistically correlated, or systematically related in any fashion. The combined power PNCoh of a set of incoherent signals {si} is the sum of

the powers of all of the individual signals, PNcoh = |s1|2 + |s2 |

2+ |s3|2 (See coherent.)

INTENSITY

Strength of a field or of a distribution, such as an image file, proportional to magnitude, squared.

INTERFEROMETER

Device such as an imaging radar that uses two different paths for imaging, and deduces information from the


coherent interference between the two signals. In SAR applications, spatial interferometry has been demonstrated to measure terrain height, and time delay interferometry is used to measure movement in the scene such as oceanic currents.

JERS-1

Satellite launched by Japan in February 1992. The payload included an L-band SAR, HH polarization and 38.5° incident angle. It also carried a stereo viewing visible and infrared optical sensor. Ref: Proceedings of the IEEE, June 1991. The satellite ceased operations in October, 1998.

JET PROPULSION LABORATORY (JPL)

Facility located at the California Institute of Technology, in Pasedena, USA. JPL is renowned for their airborne radar systems and for development of civilian SAR technology.

L-BAND

Microwave band in which the wavelengths are at or near 23.5 cm.

LAYOVER

Extreme form of elevation displacement or foreshortening in which the top of a reflecting object (such as a mountain) is closer to the radar (in slant range) than are the lower parts of the object. The image of such a feature appears to have fallen over towards the radar. The effect is more pronounced for radars having smaller incident angle.

LINEARITY

Property according to which an operation on a sum of signals is equivalent to the same operation applied to each of the signals individually, and the resulting numbers added together. If C is a multiplicative constant, then a linear operation on any two numbers x and y satisfies Cx + Cy ≥ C(x+y) + C0. (The additive constant C0 is needed to account for realistic behaviour of many practical systems that may impose a constant offset onto the sum.) Linearity, over the dynamic range of the system, is an essential attribute of most measurement devices such as imaging radars.

LOOKS

Each of the sub-images used to form the output summed image, implemented in a SAR processor. Speckle, the radiometric uncertainty in each estimate of the scene's reflectivity, is reduced by the averaging implied by adding together different detected images of the same scene. For N statistically independent looks (which may be implemented in various ways), the standard deviation of each estimate is reduced by N 1/2. Multiple looks may be generated by averaging over Nr range cells and/or Na azimuth resolution cells. For an improvement in radiometric resolution using multiple looks there is an associated degradation in spatial resolution. Note that there is a difference between the number of looks physically implemented in a processor, and the effective number of looks as determined by the statistics of the image data.

LOSS TANGENT

Ratio of the imaginary part of the dielectric constant to the real part, written as tan (tanδ = ε”/ε’). Low loss materials satisfy tan2δ<<1 .

MAGNITUDE

One of three parameters required to describe a wave. Magnitude is the amplitude of the wave irrespective of the


phase. For a complex signal described by in-phase ( I ) and quadrature (Q) components, the magnitude is given by m = (I 2 + Q 2)1/2. For complex amplitude a, magnitude is, by definition, the absolute value of amplitude, a (See detection).

MATCHED FILTER

The matched filter (first derived by North in 1942) maximizes the signal-to-noise ratio of the processor output when the input is a known signal against an additive noise background. A mathematical model of the detailed structure of a specific two-dimensional distribution, applied in a processor to cancel the phase structure of the desired set of signals.

MICROWAVE

An electromagnetic wavelength in (or near) the span 1-100 cm.

MULTI-LOOK

(See Looks.)

MOTION COMPENSATION

Adjustment of a sensing system and/or the recorded data to remove effects of platform motion, including rotation and translation, and variations in along track velocity. Motion compensation is essential for aircraft SARs, but usually is not needed for spacecraft SARs.

NADIR

Locus of points on the surface of the Earth directly below the radar as it progresses along its line of flight.

NATIONAL AERONAUTICS AND SPACE ADMINISTRATION (NASA)

American organization.

NATIONAL SPACE DEVELOPMENT AGENCY (NASDA)

Japanese organization.

NOISE

Any unwanted or contaminating signal competing with the desired signal. In a SAR, two common kinds of noise are additive (receiver) noise and signal dependent noise, usually either additive or multiplicative. The relative amount of additive noise is described by the signal-to-noise ratio. Signal dependent noises, such as azimuth ambiguities or quantization noise, arise from system imperfections, and are dependent on the strength of the signal itself. "Good" SAR systems usually keep these noise levels below acceptable levels, by design. (Speckle is sometimes considered to be a kind of signal dependent multiplicative noise in a SAR system.)

NOISE EQUIVALENT SIGMA NOUGHT (σ0Neq)

A measure of the sensitivity of a given SAR. It describes the strength of the (additive) system noise in terms of the equivalent (average) power in the image domain that would result from an idealized distributed scatterer of the stated reflectivity. Smaller noise equivalent sigma nought values are better. Within physical limitations, smaller may be achieved by increasing the power of the radar transmitter, or by decreasing the noise figure of the electronics.


NOISE FIGURE

Factor that describes the noise level in a radar receiver relative to that in a theoretically perfect receiver. The noise figure, which is always larger than one, is typically two or more, and is usually expressed in decibels.

ONE-WAY

The radar wave is emitted by the transmitting antenna, so that the antenna gain sweeps the illuminated scene. The same antenna is used for reception, and the energy backscattered by the scene is amplified by the same antenna gain for collection and processing by the radar's receiver. Thus the received pulse must travel two ways: out to each object at range R, and back again the same distance. Numbers relating to only one direction of propagation are denoted as "one-way", and the corresponding numbers that include the round trip are called "two-way". The difference between "one-way" and "two-way" is important in measuring signal phase, in measuring the effective width of an antenna pattern, and in the relationship between two-way delay time t and range distance R, such that R = ct / 2. (See speed of light and antenna)

P-BAND

As has been adopted by the SAR community, the microwave band in which the wavelengths are at or near 75 cm.

PALSAR

Phased-Array L-band Synthetic Aperture Radar, to be onboard ALOS. Incident angle range from 18 to 55 degrees. Dual polarization. Nominal 10 m resolution, depending on polarization modes and number of looks. Modes include ScanSAR, swath width 350 km.

PARALLAX

Apparent change in the position of an object due to an actual change in the point of view of observation. For a SAR, true parallax occurs only with viewpoint changes that are away from the nominal flight path of the radar. In contrast to aerial photography, parallax cannot be created by forward and aft looking "exposures". Parallax may be used to create stereo viewing of radar images.

PENETRATION

Act of microwaves entering a medium such as dry sand or forest leaf canopy. Microwave penetration, in general, is proportional to the wavelength, and inversely proportional to the loss tangent. The penetration depth Dpen for most natural materials (except highly conductive media such as water) encountered in radar remote sensing is given by Dpen = λ / (π tanδ), where λ is the wavelength, and tanδ is the loss tangent.

PERIOD

Time duration of one cycle of a wave, or of one cycle of any regularly recurring pattern. Period is inversely equal to frequency. (Units of time, such as seconds).

PERMEABILITY

Parameter that describes the magnetic properties of a material. For remote sensing applications, (magnetic) permeability, µ, is essentially the same for all materials of interest, and plays an insignificant role in image interpretation.


PERMITTIVITY

(See dielectric constant.)

PHASE

The angle ϕ of a complex number.

PHASE HISTORY

The time series of signals received and recorded by a coherent sensor, especially a SAR. Subsequent processing is used to match the phase structure of the signal in order to focus or compress the data.

PITCH

Vertical rotation of a sensor platform, in the "nose up" plane.

PIXEL

Term derived from "picture element" in a digital representation to indicate the spatial position of a sample of an image file, which consists of a spatial array of digital numbers. A two-dimensional ensemble of pixels forms the geometric grid on which an image is built. The fundamental parameter describing this grid is the inter-pixel spacing in each of the two image directions. (To confuse matters, pixel spacing is often referred to as "pixel" or "pixel size" in the literature. Pixel "size" is to be avoided.)

POLARIZATION

Orientation of the electric (E) vector in an electromagnetic wave, frequently "horizontal" (H) or "vertical" (V) in conventional imaging radar systems. Polarization is established by the antenna, which may be adjusted to be different on transmit and on receive. Reflectivity of microwaves from an object depends on the relationship between the polarization state and the geometric structure of the object. Common shorthand notation for band and polarization properties of an image file is to state the band, with a subscript for the receive and the transmit state of polarization, in that order. Thus, for example, LHV indicates L-band, horizontal receive polarization, and vertical transmit polarization. Possible states of polarization in addition to vertical and horizontal include all angular orientations of the E vector, and time varying orientations leading to elliptical and circular polarizations. (See quadrature polarization.)

POST-PROCESSING

Steps that may be applied to digital SAR image files to adjust selected attributes of the image, such as geometric accuracy or radiometric corrections, including speckle reduction and contrast enhancement, or any other form of value-added processing.

POWER

Power for a given signal is proportional to the square of its magnitude per unit time. (Units are Watts.)

PROCESSING

Sometimes denoted "preprocessing", it is the means of converting the received reflected signal into an image. Processing consists of image focusing through matched filter integration, detection, and multi-look summation. The output files of a SAR processor usually are presented with unity aspect ratio (so that range and azimuth image scales are the same). Images may be either in slant range or ground range projection. Both of these spatial adjustments require resampling of the image file.


PROPAGATION

The movement of energy in the form of waves through space or other media. Electromagnetic waves move at the speed of light c in free space, but the speed v of propagation through other materials is reduced according to the dielectric constant of the material in question, according to v = c / ( εµ )1/2.

PULSE

Group of waves with a distribution confined to a short interval of time. Such a distribution is described in the time domain (or in spatial dimensions) by its width and its amplitude or magnitude, from which its energy may be found. In radar, use is made of modulated or coded pulses which must be processed to decode or compress the original pulse to achieve the impulse response observed in the image. Coded pulses have a time-bandwidth product that is much larger than unity. The resolution that may be achieved after processing is determined by the bandwidth of the original pulse.

PULSE REPETITION FREQUENCY (PRF)

Rate of recurrence of the pulses transmitted by a radar.

QUADRATURE ( Q )

Signal component that is 90° out of phase with respect to the reference frequency.

QUADRATURE POLARIZATION ("QUAD POL") RADAR

System designed to simultaneously collect imaging data of a scene in two orthogonal polarization states on transmit and the same two polarization states on receive. From such a data set a complete scattering matrix of the reflectivity of the scene may be synthesized, leading to the concept of polarization signature. The best known example of a "quad pol" radar is the AirSAR of JPL.

RADAR

Electromagnetic sensor characterized by RAdio Detection And Ranging, from which the acronym RADAR is derived. Predicted in the early part of the 20th century, the first important system was built in England in 1938. Basic building blocks of a radar are the transmitter, the antenna (normally used for both transmission and for reception), the receiver, and the data handling equipment. A synthetic aperture radar system, by implication, includes an image processor, even though it may be remotely located in time or space from the radar electronics.

RADAR ALTIMETER

Active microwave sensor designed to measure the sea surface height (relative to the geoid) and significant wave height. State-of-the-art height measurements (eg. TOPEX/Poseidon and ERS) are accurate to a few centimeters, which requires extensive precision orbit determination and corrections for propagation delays.

RADAR CROSS SECTION (RCS)

Measure of radar reflectivity. RCS is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target. (See sigma.)

RADAR EQUATION

Mathematical expression that describes the average received signal level (or, sometimes, the image signal level) compared to the additive noise level, in terms of system parameters. Principal parameters include transmitted power, antenna gain, noise power, and radar range R. The range effect is sometimes called the spreading factor,


since effective power decreases significantly with a small increase in range. All else equal, the power received by a SAR per image pixel is proportional to R-3.

RADARSAT-2

Multi-mode C-band SAR satellite sponsored by Canada, being prepared for launch in 2003, and which incorporates all of the modes of RADARSAT-1 plus full quadrature-polarimetry and enhanced resolution.

RADAR VELOCITY

As it arises in the processing literature, the so-called radar velocity is the square root of the product of spacecraft velocity and footprint (or beam) velocity. Note that this number is a fiction, in that it does not correspond to a physical velocity. Its use is to be discouraged.

RADIOMETRIC RESOLUTION

The expected spread of variation in each estimate of scene reflectivity as observed in an image. Smaller radiometric resolution is "better". Radiometric resolution for a given radar may be improved by averaging, but at the cost of spatial resolution. (See looks.)

RANGE

Line of sight distance between the radar and each illuminated scatterer (see one-way). In SAR usage, the term is also applied to the dimension of an image away from the line of flight of the radar. (See slant range and ground range.)

RANGE AMBIGUITIES

Unwanted echoes that fall into the image from positions that in fact are outside of the intended swath, due to the range sampling operation of the radar. Range ambiguities may be minimized by antenna pattern and imaging mode control and are observed only rarely in imagery from well designed systems.

RANGE CURVATURE

Describes the changing distance between the radar and an object during the time that the object is illuminated by the antenna. Range curvature is more important for long range systems such as satellite SARs, and must be compensated in the processor as a part of image focusing.

RANGE RESOLUTION

Resolution characteristic of the range dimension, usually applied to the image domain, either in the slant range plane or in the ground range plane. Range resolution is fundamentally determined by the system bandwidth in the range channel.

RAR (REAL APERTURE RADAR)

Real aperture radar, as opposed to SAR. Real aperture implies that the cross-range resolution is given by the product of beamwidth and radar range. Beamwidth is inversely proportional to aperture size.

REFLECTIVITY

Property of illuminated objects to reradiate a portion of the incident energy. Reflectivity, in general, is larger in the specular direction for smaller surface roughness. For side looking radars, backscatter is the observable portion of the energy reflected. Backscatter, in general, is increased by greater surface roughness. In general, reflectivity is increased for higher conductivity of the scattering surface. The relative strength of radar reflectivity is tabulated by sigma, for discrete objects, and by sigma nought for natural terrain surfaces.


REFLECTIVITY, COMPLEX COEFFICIENT OF

Ratio of the complex amplitude of the reflected electric component to the incident electric component of an electromagnetic wave at a surface orthogonal to the incoming illumination.

RELIEF DISPLACEMENT

Alternative term for elevation displacement.

RESOLUTION (Radar)

Generally (but loosely) defined as the width of the "point spread function", the "Green's function", or the " impulse response function", depending on whether one has an optics, a physics, or an electronic systems background. More properly, "resolution" refers to the ability of a system to differentiate two image features corresponding to two closely spaced small objects in the illuminated scene when the brightness of the two objects in question are comparable and fall within the dynamic range of the radar in question. (Definition adapted from Lord Rayleigh [1879]). "Higher resolution" refers to a system having a smaller impulse response width.

RESOLUTION CELL

A three-dimensional cylindrical volume surrounding each point in the scene. The cell range depth is slant range resolution, its width is azimuth resolution, and its height, which is conformal to the illumination wavefront, is limited only by the vertical beam width of the antenna pattern. Resolution cell often is defined with respect to the local horizontal. (See ground range).

ROLL

Rotation of a sensor platform around the flight vector, hence in a "wing down" direction.

ROUGHNESS

Variation of surface height within an imaged resolution cell. A surface appears "rough" to microwave illumination when the height variations become larger than a fraction of the radar wavelength. The fraction is qualitative, but may be shown to decrease with incident angle.

SAR

Synthetic Aperture Radar, so-called because azimuth resolution is achieved through computer operations on a set of (coherently recorded) signals such that the processor is able to function like a large antenna aperture in computer memory, thus realizing azimuth resolution improvement in proportion to aperture size. The SAR concept was introduced by C. Wiley (USA) in 1951.

S-BAND

Microwave band in which the wavelengths are at or near 10 cm.

SCATTERING MATRIX

Array of four complex numbers that describes the transformation of the polarization of a wave incident upon a reflective medium to the polarization of the backscattered wave. It is the polarization vector counterpart to the coefficient of reflectivity.

SCENE

Object space; that part of the Earth's surface illuminated by the radar for which image acquisition may occur.


SEASAT

NASA ocean research satellite that was in operation July-September of 1978. SEASAT was the first (civilian) satellite to carry a SAR. It operated at L-band, using horizontal polarization at 22° incident angle. Data from SEASAT is still important for applications and processing technique development.

SENSITIVITY TIME CONTROL (STC)

Pre-programmed change in radar amplitude due to weaker backscatter from greater ranges and varying incident angles across the imaged swath.

SHADOW

From an optical point of view as seen from the position of a radar, a region hidden behind an elevated feature in the scene would be out of sight. This region corresponds to that which does not get illuminated by the radar energy, and thus is also not visible in the resulting radar image. The region is filled with "no reflectivity", which appears as small digital numbers, or a dark region in hard copy.

SIDELOBES

Non-zero levels in a distribution that are separated from the desired central response. Sidelobes arise naturally in antenna patterns, although in general, they are considered to be a nuisance, and must be suppressed as much as possible. Large side-lobes may lead to unwanted multiple images of a single feature.

SIGMA ( σ )

The conventional measure of the strength of a radar signal reflected from a geometric object (natural or manufactured) such as a corner reflector. Sigma specifies the strength of reflection in terms of the geometric cross section of a conducting sphere that would give rise to the same level of reflectivity. (Units of area, such as metres squared). (See radar cross section.)

SIGMA NOUGHT (σ° )

Scattering coefficient, or the conventional measure of the strength of radar signals reflected by a distributed scatterer, usually expressed in dB. It is a normalized dimensionless number, comparing the strength observed to that expected from an area of one square metre. Sigma nought is defined with respect to the nominally horizontal plane, and in general has a significant variation with incident angle, wavelength, and polarization, as well as with properties of the scattering surface itself. (See speckle, statistics.)

SIGNAL

Generalized terminology used to signify a mathematical description of a wave, pulse, or other sequence of interest. It often suggests the ensemble of data corresponding to observed scattering from the scene, either before reception, within the radar or processor, or in the image file. Normally there is a distinction between "signal" and noise.

SIR-A (SHUTTLE IMAGING RADAR-A)

NASA sponsored radar mission in the Shuttle, lasting about one week. SIR-A (November 1981) was at L-band, HH polarization, nominally 50° incident angle, and was optically processed.

SIR-B (SHUTTLE IMAGING RADAR-B)

NASA sponsored radar mission in the Shuttle, lasting about one week. SIR-B (October 1984) was at L-band, HH polarization, offered a variety of incident angles from about 20° to 50°, and was digitally processed.


SIR-C (SHUTTLE IMAGING RADAR-C)

A Shuttle radar used for missions in the 1990s.

SLANT RANGE

Image direction as measured along the sequence of line-of-sight rays from the radar to each and every reflecting point in the illuminated scene. Since a SAR looks down and to the side, the slant range to ground range transformation has an inherent geometric scale which changes across the image swath. (See ground range.)

SLAR

Side-looking airborne radar, a term originally coined in the late 1940’s to describe a real aperture radar configured to generate imagery using side-looking geometry. The term sometimes is invoked as an antonym to SAR, but strictly interpreted, a SAR is one type of SLAR. To remove this ambiguity, the term RAR was introduced.

SPACECRAFT VELOCITY

Physical velocity of the spacecraft along its orbital path. The spacecraft velocity of a satellite in low-earth orbit (near 800 km altitude) is about 7.4 km/s.

SPECKLE

Statistical fluctuation or uncertainty associated with the brightness of each pixel in the image of a scene. A single look SAR system achieves one estimate of the reflectivity of each resolution cell in the image. Speckle may be reduced, at the expense of resolution, in the SAR processor by using several looks. Speckle appears as a multiplicative random process whose variance and spatial correlation are determined primarily by the SAR system.

SPECULAR

Coherent reflection from a smooth surface in a plane normal to the surface at an angle opposite to the local incident angle. (From speculum, mirror in Latin.)

SPEED OF LIGHT ( c )

Approximately 300,000,000 metres per second. This is the speed of light in "free space", a condition typical of electromagnetic propagation through most atmospheric conditions found on Earth. Denser media, such as the atmosphere of Venus, that have a low loss dielectric constant, retard the speed of propagation according to their material properties.

SpotSAR (SPOTLIGHT MODE)

SAR imaging mode in which the antenna pattern is skewed so that only one (small) area is illuminated as the radar passes. The benefit is that the data collected has very large Doppler bandwidth, which can be converted into very fine resolution. The disadvantage is that only the illuminated area is imaged. Adjacent regions that are not illuminated cannot be imaged.

SQUINT MODE

SAR imaging mode in which the antenna pattern is maintained at an angle that is not orthogonal to the line-of-flight. The most common configuration has the antenna pointed towards the nose of an aircraft, sometimes as little as ten degrees with respect to the forward velocity vector. Since RADARSAT’s antenna is pointed orthogonal to the satellite’s line-of-flight, Earth rotation imposes an effective squint angle up to ±3 degrees relative to the zero-Doppler plane.


SRTM (SHUTTLE RADAR TOPOGRAPHY MISSION)

The Shuttle Radar Topography Mission, which flew February 11-22, 2000, was a dedicated re-flight of the SIR-C / X-SAR hardware, augmented by C-band and X-band antennas mounted on a 60 m boom extended orthogonal to the slant range plane, thus to form a real-time cross-track interferometer. The mission gathered data for eight days, collecting topographic data for all of the Earth’s land mass that was within view of the radar.

STATISTICS

Set of numbers that describes average properties of a random process. For example, σ0 is the reflectivity observed from a uniform target with a two-dimensional surface, say x and y. Each observation of σ0 (x,y) is a sample function having a variety of values at each location due to speckle. The probability distribution function is determined primarily by the number of independent looks used in the processor (NL ). The average value of the

corresponding image brightness is the mean reflectivity σ 0, and the average difference between the highest and lowest brightness values is given by the standard deviation, σ0/ √NL.

STOKES MATRIX

A description of the complete polarization signature of a reflective medium. 4x4 array of real numbers that describes the transformation of the Stokes parameters of the incident wave into the Stokes parameters of the electromagnetic wave reflected by each element of a scene illuminated by a radar.

STOKES PARAMETERS

Set of four real numbers that together describe the state of polarization of an electromagnetic wave.

STRIP-MAP MODE

The default side-looking imaging radar configuration, wherein the antenna illumination pattern is maintained to be orthogonal to the radar’s line-of-flight. Note that for a satellite SAR, the spacecraft velocity vector is offset from the ground-track vector as a consequence of Earth rotation.

SWATH

Width, in the range direction, of the imaged portion of a scene.

TEXTURE (Radar)

Second order spatial average of brightness. Scene texture is the spatial variation of the average reflectivity. For areas of nominally constant average reflectivity, image texture consists of scene texture multiplied by speckle.

TIME-BANDWIDTH PRODUCT (TBP)

Parameter found from the width of a distribution in the time (or space) domain multiplied by the width of the same distribution observed in the frequency domain. (Typically, the azimuth [Doppler modulated] signal and the range chirp coded pulse each have TBP larger than 100.)

TONE

First order spatial average of image brightness, often defined for a region of nominally constant average reflectivity.


TRANSMISSION

Energy sent by the radar, normally in the form of a sequence of pulses, to illuminate a scene of interest.

TRIHEDRAL

Corner reflector formed from three mutually orthogonal surfaces.

VOLTAGE

Standard unit of magnitude of an electrical signal, named after Count A. Volta, inventor of the battery (about 1800).

VOLUME SCATTERING

Multiple scattering events occurring inside a medium, generally neither dense nor having a large loss tangent, such as the canopy of a forest. The relative importance of volume scattering is governed by the dielectric properties of the material.

WAVE

Propagating periodic displacement of an energy field. A surface wave on the water serves to visualize the key properties of an electromagnetic wave. At any instant of time, a wave is described by its "height" (amplitude) and its "length" (wavelength). Equally important is the phase of the wave, which is the number that describes the position of the "crests" or "troughs" with respect to a given reference position. At any specific location in space, propagation of the wave occurs and its frequency may be observed. A wave propagates within a given medium at a speed given by the product of its wavelength and its frequency. In radar, waves are very well represented by families of sinusoidal functions, so-called harmonic oscillation.

WAVEFRONT

Three dimensional surface in space for which the field radiated by an antenna has the same phase at all points. At a distance R far from an antenna, the wavefront is a spherical surface with radius R over the angular window established by the antenna pattern. For most geometries encountered in remote sensing, the wavefront may be approximated by a plane tangent to the spherical surface, within a tolerance of much less than a wavelength over a spatial scale of several resolution cells.

WAVELENGTH (λ)

Minimum distance between two events of a recurring feature in a periodic sequence, such as the crests in a wave. (Units of length, such as metres).

WAVENUMBER (k)

By convention, the ratio 2π /λ where λ is the wavelength.

WIDTH, EQUIVALENT RECTANGLE

A standard definition to measure the effective width of a distribution. The width is that of a rectangular distribution with the same amplitude as the maximum of the distribution, and having the same area in the rectangle as is in the measured distribution.

WIDTH, 3dB

One representation of the impulse response width, which defines the spatial resolution of a radar system. The


term 3 dB refers to the width of a pulse at its half power level which is the power level 3 dB below the power at the peak.

X-BAND

Microwave band with wavelength at or near 3 cm.

YAW

Rotation of a sensor platform in the horizontal plane, hence in a "nose right" direction.

ZERO-DOPPLER PLANE

Surface in space within which the relative velocity between the radar and the scene is zero. Note that in orbital geometry, the zero-Doppler plane is orthogonal to the satellite’s ground track, and in general is not orthogonal to the satellite’s velocity vector.


Acronyms

ADC Analog to Digital Converter AGC Automatic Gain Control ALE Absolute Location Error ASL Above Sea Level CCRS Canada Centre for Remote Sensing CCT Computer Compatible Tape CEOS Committee on Earth Observation System CSA Canadian Space Agency DEM Digital Elevation Model dB Decibel DN Digital Number DTM Digital Terrain Model EM Electromagnetic ERS-1 European Remote Sensing Satellite FFT Fast Fourier Transform GCP Ground Control Point HH Mode of Polarization: Horizontal Transmit - Horizontal Receive HV Mode of Polarization: Horizontal Transmit - Vertical Receive IHS Intensity, Hue and Saturation Colour Space IRW Impulse Response Width LUT Look Up Table MDC Minimum Distance Classifier MLC Maximum Likelihood Classifier PC Parallelepiped Classifier PRF Pulse Repetition Frequency PSLR Peak Side Lobe Ratio RGB Red, Green and Blue Colour Space

Page 1 of 2Acronyms

RMS Root Mean Square RSI RADARSAT International Inc. SAR Synthetic Aperture Radar SCN ScanSAR Narrow SCW ScanSAR Wide SD Standard Deviation SGF SAR Georeferenced Fine Resolution SGX SAR Georeferenced Extra-Fine Resolution SLC Single Look Complex SNR Signal to Noise Ratio SPG SAR Precision Geocoded SSG SAR Systematically Geocoded VIR Visible and Infrared (Portion of the EM Spectrum) VV Mode of Polarization: Vertical Transmit - Vertical Receive

Page 2 of 2Acronyms

Acknowledgements

The development and production of this CD-ROM was made possible through funding and support from the Canadian International Development Agency and the International Development Research Centre, under the GlobeSAR 2 Program. This regional program focused on radar training for natural resource management and environmental monitoring in Latin America.

The Canada Centre for Remote Sensing wishes to thank all those who have contributed to the development of this radar remote sensing training package.

In particular, Dr. Brian Brisco of Noetix Research Inc. played a key role in the development of this training package, and is gratefully acknowledged. The following individuals also made valuable contributions: Dr. Ian Cumming (University of British Columbia), Dr. R. Keith Raney (Johns Hopkins University), Mr. Scott Paterson (Dendron Resource Surveys Inc.) and M. Thierry Fisette (MIR Télédétection Inc.).

The Canada Centre for Remote Sensing would also like to thank Dr. Francisco J. Ocampo-Torres (Centro de Investigación Cientifica y de Educación Superior de Ensenada, Mexico) for reviewing the Spanish version of the materials, Dr. Edson Sano (Empresa Brasileira de Pesquisa Agropecuária, Brazil) for reviewing the Portuguese version, and M. Robert Saint-Jean (MIR Télédétection) and Mme. Caroline Forest (First Mark Technologies) for reviewing the French version.

RADARSAT images found in this material are copyright of the Canadian Space Agency. The images were received at the Canada Centre for Remote Sensing (CCRS) and processed by RADARSAT International Inc (RSI). Image interpretation and analysis were performed at CCRS, except where noted otherwise.

Acknowledgement is also given to the generous contributions and advice of Scientists and Multimedia Specialists at the Canada Centre for Remote Sensing. Our partners in the implementation of the GlobeSAR-2 Program are also acknowledged: Radarsat International, PCI Geomatics, Atlantis Scientific Inc., and the University of Sherbrooke.

CCRS specially thanks the many scientists and organizations who have kindly permitted us to reproduce various illustrations, imagery and graphs in this CD-ROM. Every effort has been made to correctly acknowledge these sources. Information that would allow us to rectify any errors or omissions is welcome and would be incorporated in any subsequent releases.

Michael Henschel, Satlantic Inc. Wolfhard Geile, Geomatics Consulting H.M. Gansen, Intermap Technologies Inc. John R. Jensen, University of South Carolina John Molendyk, PCI Geomatics Inc. Fawwaz T. Ulaby, University of Michigan Marco van de Kooij, Atlantis Scientific Inc. Yong Wang, East Carolina University

Educational resources for radar remote sensing were developed for a series of workshops in the GlobeSAR-2 Latin American countries. The quality of the final materials is largely attributable to the evaluations and feedback from the attendees of those workshops. For the organisation of the workshops and national seminars in each country, a special thank you to all the GlobeSAR 2 co-ordinating agencies.

Page 1 of 2Acknowledgements

http://www.noetix.on.ca/

http://www.ece.ubc.ca/sar/

http://www.jhuapl.edu/

http://www.dendron.com/

http://www.mirteledetection.com/

http://www.cicese.mx/

http://www.embrapa.br:8080/aplic/fontes.nsf/08310abb4d3092b50325656700693cea/af00e0ee8fb9f2a503256a8e0063e131?OpenDocument

http://www.embrapa.br:8080/aplic/fontes.nsf/08310abb4d3092b50325656700693cea/af00e0ee8fb9f2a503256a8e0063e131?OpenDocument

http://www.ccrs.nrcan.gc.ca/

http://www.atlsci.com/

http://www.rsi.ca/

http://www.pcigeomatics.com/

http://www.usherbrooke.ca/

Permission for Use

Educators are encouraged to use the material for their own teaching needs, but it must be clearly indicated that the Canada Centre for Remote Sensing is the originator of this material and appropriate credit must to given to the authors at all times. These documents may be reproduced in whole, for training and educational purposes, but not for commercial exploitation. CCRS reserves the right of distribution of this material. Requests for further copies may be directed to the Canada Centre for Remote Sensing GlobeSAR Program.

GlobeSAR Program Canada Centre for Remote Sensing Natural Resources Canada 588 Booth Street Ottawa, Ontario K1A 0Y7 CANADA E-mail: [email protected] WWW: http://www.ccrs.nrcan.gc.ca/ccrs/rd/programs/globsar/gsarmain_e.html

Argentina CONAE Comisión Nacional de Actividades Espaciales http://www.conae.gov.ar/caratula.html

Bolivia ABTEMA Asociación Boliviana de Teledetección para el Medio Ambiente http://condesan.org/socios/abtema/a_uno.htm

Brazil INPE Instituto Nacional de Pesquisas Espaciais http://www.inpe.br/

Chile PUCC Pontifícia Universidad Católica de Chile, Facultad de Agronomía e Ingeniería Forestal, Centro de Percepción Remota y Sistemas de Información Geográficos http://www.cprsig.puc.cl/

Colombia IGAC Instituto Geográphico Agustín Codazzi http://www.igac.gov.co/

Costa Rica IGN Instituto Geográphico Nacional http://www.casapres.go.cr/

Honduras AFE COHDEFOR Corporación Hondureña de Desarrollo Forestal, Administración Forestal del Estado http://rds.org.hn/docs/membresia/directorio/per-gob/afecoh.htm

Panama DGRM Ministerio de Comercio e Industrias, Dirección General de Recursos Minerales http://www.mici.gob.pa/consecmin.html

Peru CONIDA Comisión Nacional de Investigación y Desarrollo Aeroespacial http://www.conida.gob.pe/

Uruguay CeCal Universidad de la República, Facultad de Ingeniería, Centro de Calculo http://www.fing.edu.uy/cecal/cecal.html

Venezuela CPDI Instituto de Ingeniería, Centro de Procesamiento Digital de Imágenes http://www.fii.org/webfii/cpdi/cpdi.htm

Page 2 of 2Acknowledgements

http://www.conae.gov.ar/caratula.html

http://condesan.org/socios/abtema/a_uno.htm

http://www.inpe.br/

http://www.cprsig.puc.cl/

http://www.igac.gov.co/

http://www.casapres.go.cr/

http://rds.org.hn/docs/membresia/directorio/per-gob/afecoh.htm

http://www.mici.gob.pa/consecmin.html

http://www.conida.gob.pe/

http://www.fing.edu.uy/cecal/cecal.html

http://www.fii.org/webfii/cpdi/cpdi.htm




Documents

Educational Resources for Radar Remote Sensing