Tutorial: Introduction to Interpreting Digital RADAR Images · in the tutorial booklets Displaying Geospatial Data and TNT Product Concepts. ... volcanic mudflow deposits ... Interpreting

Interpreting Digital Radar Images

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Interpreting DigitalRADAR Images

with

TNTmips®

Introduction toRADAR

INTERPRET


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Before Getting StartedAirborne and satellite radar systems are versatile imagery sources with day-night,all-weather capability. Radar imagery will likely increase in importance in thefuture as new multiwavelength, multipolarization radar systems are deployed thatallow interpreters to better discriminate between different surface materials.However, radar images have unique characteristics that require specialized toolsfor proper processing and interpretation. This booklet introduces the concepts ofradar image interpretation and gives you an overview of the processes in TNTmips®

that enable you to process and interpret radar imagery.

Prerequisite Skills This booklet assumes that you have completed the exercisesin the tutorial booklets Displaying Geospatial Data and TNT Product Concepts.Those exercises introduce essential skills and basic techniques that are notcovered again here. You will also find important background information in thebooklet Introduction to Remote Sensing of Environment. Please consult thosebooklets for any review you need.

Sample Data This booklet does not use exercises with specific sample data todevelop the topics presented. Rather, it provides guidelines for using TNTmips®

to work with your own digital radar imagery.

More Documentation This booklet is intended only as an introduction to radarinterpretation techniques. Details of the processes discussed here can be found ina variety of tutorial booklets, Technical Guides, and Quick Guides, which are allavailable from MicroImages’ web site (go to http://www.microimages.com/searchto quickly search all available materials, or you can narrow your search to includeonly tutorials or plates).

TNTmips® Pro and TNTmips Free TNTmips (the Map and Image ProcessingSystem) comes in three versions: the professional version of TNTmips (TNTmipsPro), the low-cost TNTmips Basic version, and the TNTmips Free version. Allversions run exactly the same code from the TNT products DVD and have nearlythe same features. If you did not purchase the professional version (which requiresa software license key) or TNTmips Basic, then TNTmips operates in TNTmipsFree mode.

Randall B. Smith, Ph.D., 4 January 2012©MicroImages, Inc., 2001-2012

You can print or read this booklet in color from MicroImages’ Web site. TheWeb site is also your source for the newest tutorial booklets on other topics.You can download an installation guide, sample data, and the latest versionof TNTmips.

http://www.microimages.com


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Interpreting Digital Radar ImagesAircraft-mounted imaging radar systemshave been in use for several decades.Early systems processed the recorded datato create an image on film, but modernsystems record and process radar imagedata in digital form. With the launch ofcommercial radar imaging satellites dur-ing the past two decades (Canada’sRADARSAT and the European SpaceAgency’s ERS-1 and ERS-2), digital ra-dar images are becoming more widelyavailable.

Unlike most remote sensing systems, which rely onthe sun as an energy source, imaging radar systemsare active sensors that “illuminate” surface featureswith broadcast microwave energy and record a re-turned signal. Most imaging radar systems producemicrowaves with wavelengths between 1 cm and 1meter, longer than the wavelengths used in weatherradar to detect rain and snow. These longer wave-length microwave signals propagate through theatmosphere and clouds with almost no interactionor weakening. As a result imaging radar systemscan be used to map surface features day or night andin almost any weather conditions. These character-istics make radar imaging especially useful in tropicaland polar regions where persistent cloud cover ham-pers optical remote sensing efforts.

Radar images can reveal information about the shapeof the surface terrain as well as its physical and bio-physical properties. Radar images have long beenused in geological studies to map structural featuresthat are revealed by the shape of the landscape. Sat-ellite radar images are now routinely used to monitorarctic sea ice conditions and to detect oil slicks onthe ocean surface. Radar imagery also has applica-tions in vegetation and crop type mapping,landscape ecology, hydrology, and volcanology.

Acquisition and import of ra-dar images are discussed onpages 4-5. Pages 5-9 covercontrols on radar image bright-ness and methods of contrastenhancement. The role ofwavelength and polarizationmode in determining radarimage characteristics is intro-duced on pages 10-13.Pages 14-16 discuss radarimage noise and its reduction,while pages 17-18 explaingeometric distortions in radarimages. Common sources ofradar images are listed onpage 19.

Color composite radar imageof Mount Pinatubo volcanoand surroundings in the Phil-ippines, acquired by theSpaceborne Imaging Radar-C instrument carried by theNASA space Shuttle Endeav-our in April 1994. The imagewas created from differentradar wavelength and polar-ization bands. Red areasshow rough ash deposits, andthe black areas are smoothvolcanic mudflow depositsalong rivers.


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Importing Radar ImagesBefore you can begin interpreting a digital radarimage, you must first import the image into aTNTmips Project File by using the Import process.TNTmips provides direct import of radar data fromthe following sensor formats:

STEPSchoose Main / Importfrom the TNTmips menupress the Raster iconbutton on the ImportFormat panel of theImport windowselect the formatappropriate for yourradar imagepress [Select Files] andselect your image fileset any additionalrequired parameters onthe Import Rasterwindowpress [Import], thenspecifiy a destinationand name for the newraster object

In some of these formats the main image file isaccompanied by a separate header or leader file thatis also required by the import process. Be sure tokeep all supplied files together when importing radarimagery.

If you have radar images in other file formats, youcan import them using the User-Defined format op-tion. When you pick this option, you must create aformat file specifying the structure of the image file,the numerical data type, byte order, and other infor-

mation that canusually be foundin the imageheader files oraccompanyingdocumentation.

ERS-SAR: European Space Agency ERS-1 andERS-2 satellites

RADARSAT: Canada RADARSAT satellite


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Radar Image Acquisition

Depressionangle

Range

(or Look)

Direction

An imaging radar systememits discrete radar pulsesthat are directed to one side,so that each pulse illuminates astrip of terrain perpendicular to theflight path. The microwaves inter-act with surface objects andsome portion of them return to-ward the radar antenna. Thesystem records the pulse “echoes”from the terrain and their variation instrength with travel time. Since the mi-crowaves travel at the speed of light in air,the timing of each portion of the returned sig-nal establishes its image position in the rangedirection (perpendicular to the flight line). The im-age is built up from the returns of successive pulses as the aircraft or spacecraftmoves forward (in the azimuth direction).

An important element of the radar imaging geometry is the depression angle,defined as the vertical angle between a horizontal plane and a straight line con-necting the radar antenna and a particular terrain feature. (This line represents thetwo-way travel path of the energy returned from that feature.) The depressionangle is steepest for the portion of the image nearest the flight path (near range),and decreases toward the far range.

Different factors control the spatial resolution of a radar image in the range andazimuth directions. The range resolution depends primarily on the brief durationof the microwave pulse (measured in microseconds). Range resolution also im-proves toward the far range as the depression angle becomes smaller. Resolutionin the azimuth direction depends fundamentally on the width of the beam pro-duced by a single microwave pulse; the narrower the beam, the better the resolution.In early airborne radar systems the beam was narrowed by increasing the physicallength of the radar antenna, but there are obvious practical limits to antenna size.Modern Synthetic Aperture Radar (SAR) systems use a short physical antenna,but complex processing of multiple radar returns creates the effect of a muchlonger “synthetic” antenna. A particular surface feature is “imaged” by pulses ata number of successive antenna positions. The relative motion between the an-tenna and each target modifies the returning signal in such a way that the datafrom the various pulses can be resolved to place each feature in its correct posi-tion with good azimuth resolution.

Azimuth Direction

Radar image


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Roughness and Radar Image BrightnessAreas with stronger radar re-turns are shown as brighterareas in a radar image. Becauseof the side-looking geometry ofimaging radar systems, only asmall portion of the broadcastenergy returns back in the direc-tion of the antenna and isdetected by it. The strength ofthe radar return depends on anumber of factors, including theorientation of the surface, itsroughness and electrical prop-erties, and the polarizationdirection of the returning radarwave.

For areas that are relatively flat,surface roughness is the primary characteristic thatdetermines the radar signature. A smooth horizontalsurface, such as a calm water body, acts like a mirrorto produce a specular reflection (see illustration atleft). Almost all of the radar energy is reflected at anangle equal to the angle of incidence, which meansthat the reflection is directed away from the radarantenna. Since little energy returns toward the radarantenna, smooth areas appear very dark, like the riverin the illustration above.

When the broadcast radar signal encounters a roughersurface, the irregularities on the surface act to scat-ter the radar energy in many directions. A smallportion of the scattered energy is directed toward theradar antenna, where it is detected and produces abright signature in the image. Brightness thereforeincreases with the degree of surface roughness.

Radar image (left) andreflected light satellite image(right) of the same terrain.Arrow shows radar lookdirection. Radar image hasbeen projected to groundrange (see page 17).

Smooth Surface:Specular Reflection

(no radar return)

i r

Angle i = Angle r

Rough Surface:Scattering


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Other Controls on Radar Brightness

Corner Reflector(side view)

Terrain shape also produces sig-nificant brightness differences dueto the varying orientation of the lo-cal surface relative to the radartravel path. If other factors areequal, the strongest returns willcome from surfaces that are per-pendicular to the travel path, ordirectly facing the sensor. Slopesthat face away from the sensor will produce weakerreturns. If the back slopes of hills and ridges aresteeper than the local depression angle, they are notilluminated by the radar pulse, and are completelyshadowed. The shadow effect increases toward thefar range as the depression angle becomes smaller,causing more gentle backslopes to become shad-owed. The long mountain ridges in the images shownon the previous page are very evident in the radarimage due to the combination of bright foreslopesand darker backslopes.

Buildings in populated areas usually look bright inradar images because of their shape. The planarbuilding sides intersect the surrounding ground at aright angle, creating a corner reflector. As shownin the illustration to the right, the radar signal bouncesoff of both planar surfaces and is reflected directlyback toward the antenna, regardless of the depres-sion angle. Corner reflectors are sometimesconstructed in areas to be imaged by radar as a meansof calibrating the strength of the returning signal.

Metal objects such as powerline towers and bridgesappear very bright in radar images because they havehigh values of a property called the dielectric con-stant. Most dry natural materials have low dielectricconstants, but the presence of moisture in soil, snow,or vegetation increases this value and their radarreflectivity. Radar imagery can thus play a role inassessing moisture content of surface materials.

Radar shadowon backslopesteeper thandepressionangle

Weak returnfrombackslope

Strongreturn fromforeslope

Buildings are very bright in aradar image of a townbecause the building sidesact as corner reflectors.

A stong signal is directedback toward the radarantenna regardless of thedepression angle.


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Enhancing Radar Image Contrast

AIRSAR image of a town and surroundingagricultural fields shown with linear contrastenhancement and the upper input rangelimit greatly lowered. Image is from theSalinas Valley, California USA. Buildings inthe town and a few fields are at or nearmaximum brightness, while most fields aredark and show poor contrast.

Sample histograms from typical radar images ofland surfaces. Above, an early Spring RADARSATimage from North Dakota, USA, an agriculturalarea. Below, an AIRSAR image from westernNebraska, USA, an area with both cropland andgrassland.

The strength of the microwaveenergy scattered back from theground to a radar sensor variesgreatly. While the signal re-ceived from the majority of theground surface is typicallyweak, there are often a few ob-jects in the scene that, becauseof their geometry or electricalproperties, return an extremelystrong backscattered signal. Asa result, the numerical valuesin a digital radar image mayrange over several orders ofmagnitude, but the majority ofthe values are at the low end ofthe range. The illustration tothe left shows several examplesof radar image histograms.

Because of this distribution of bright-ness values, a radar image displayedwith no contrast enhancement or withAuto-Linear enhancement typicallylooks mostly black with only a fewbright features. You can improve thelinear contrast using the TNTmipsContrast Enhancement procedure bylowering the upper input range limit,as shown in the image to the left. Con-trast for the areas with lower cell valuesis enhanced, but all of the cell valuesabove the input limit are shown atmaximum screen brightness. You canobtain even better contrast for radarimages by using one of the nonlinearenhancement methods, as discussed onthe following page.


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Nonlinear Contrast Enhancements

Exponential Contrast Enhancement

Auto-Normalize Contrast Enhancement

Auto-Equalize Contrast Enhancement

TNTmips offers several nonlinear con-trast enhancement methods, includingnormalized, equalized, logarithmic, andexponential. You can use these meth-ods either as auto-contrast options fromthe Contrast menu in the Raster LayerControls window, or select and manipu-late them in the Raster ContrastEnhancement window.

Although these nonlinear enhancementmethods are available for any type ofimage, they are ideally suited to theunique brightness distribution of radarimages. The normalized method deter-mines output brightness values byfitting input values to a normal(gaussian) curve. The equalized methodattempts to assign equal numbers ofcells to each output brightness level.The logarithmic method computes out-put values using a logarithmictranslation function. The exponentialmethod is more flexible than the loga-rithmic because you can modify thepower of the exponential function bydragging the translation curve. A powervalue less than 1 is usually appropriatefor radar images. For more informa-tion on using these enhancementmethods, see the tutorial booklet Get-ting Good Color, or the TNTmipsReference Manual.

Which contrast enhancement method is“best” will vary from image to imagedepending on the particular distributionof raster values and the portion of thedata range that is relevant to your inter-pretation objectives.


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Roughness and Radar Wavelength

AIRSAR images at three wavelengths from a small area on the south flank of Kilauea volcano,Hawaii, USA. The look direction is toward the bottom of the images, which coincides withthe downward slope direction. Land cover was interpreted from maps and other imagery.Ancient lava surfaces are vegetated, with forest at higher elevations and grass and shrubsat lower elevations. Two types of recent, unvegetated lava flows are present: fluid, smooth-surfaced flows and more viscous flows with a rough blocky surface. In the C-Band image,only the smooth recent lavas appear dark; the other surfaces are rough at this wavelength.At the longer wavelengths the grassland area becomes smooth and thus dark as well. Therough lavas and forest have similar brightness at all three of these radar wavelengths.

Wavelength bands commonly used inimaging radar systems. Band designatorsare former military code letters.

Usual WavelengthBand Wavelength RangeK 0.8 to 2.4 cmX 3 cm 2.4 to 3.8 cmC 6 cm 3.8 to 7.5 cmS 8 cm, 12.6 cm 7.5 to 15 cmL 23.5 cm 15 to 30 cmP 68 cm 30 to 100 cm

Surface roughness differences areresponsible for much of the brightnessvariation seen in radar images.Roughness refers to the degree ofirregularity and vertical relief ofsurfaces, at scales measured incentimeters. Whether a surfaceappears rough (bright) in a radar imagedepends to a large extent on the radarwavelength. A particular surface may

be rough for a short-wavelength radar but smoothfor longer wavelengths, as illustrated by the examplebelow.

grass

forest

smoothlavafault scarps

rough lava

C-Band VV (wavelength = 6 cm) L-Band VV (wavelength = 23.5 cm)

P-Band VV (wavelength = 68 cm) Interpretation


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Plane-polarized Radar WaveElectric field oscillating in single plane

Direction ofwave travel

Radar Polarization and Scattering TypesImaging radar systems typically trans-mit a plane-polarized radar pulse. Theelectric field associated with such a pulseoscillates in a single plane perpendicu-lar to the direction the wave is traveling.The most common polarization mode isto both transmit and receive horizontallypolarized signals (designated HH mode,with the first letter indicating the trans-mitted polarization). Some systems transmit andreceive vertically polarized waves (VV mode); theradar images of Kilauea shown on the previous pagewere acquired in this mode. Both HH and VV modescan be called like-polarized radar modes.

When a polarized radar pulse is scattered by the roughsurface of a material such as soil or rock (surfacescattering), most of the scattered energy that returnsto the antenna has the same polarization as thetransmitted pulse. But when the pulse strikesvegetation, it penetrates to varying degrees(depending on wavelength) and has numerousencounters with leaves, twigs, and branches thatproduce many scattering events. Although thephysical mechanism is not well understood, thisvolume scattering causes partial depolarization ofthe radar signal, so that some of the scattered wavesvibrate in various directions. Depolarizationsomewhat reduces the strength of the like-polarizedsignal returned to the radar antenna, but vegetationstill produces strong like-polarized signals atappropriate wavelengths, as shown by the images onthe previous page.

Short-wavelength radar signals (X and C bands)interact primarily with the uppermost leaf canopy,and do not penetrate beyond it. Longer-wavelengthradars (L and P bands) penetrate deeper to interactwith stems of smaller plants and twigs and branchesof trees.

V

H

Volume Scattering

Surface Scattering


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Polarimetric Radar ImagesSome experimental radar systems cantransmit and receive signals with eithervertical or horizontal polarizationplanes. This capability enables them tosimulaneously acquire images withdifferent send-receive polarizationcombinations. In addition to theconventional HH and VV like-polarization images, such systems canalso produce cross-polarized radarimages by pairing transmission at onepolarization with recording at the otherpolarization (HV and VH modes).

Cross-polarized radar images candiscriminate between areas of surfacescattering and volume scatteringbecause of the differing depolarizationeffects associated with thesemechanisms. Surface scattering does

not cause significant change in polarization, so thecross-polarized receiving antenna receives littleenergy from areas of bare soil and rock. These areasappear relatively dark in a cross-polarized radarimage, though brighter than radar-smooth surfaces.For vegetated areas, significant depolarization of theradar signal occurs because of volume scattering. Thecross-polarized receiving antenna detects the smallfraction of the depolarized radar energy that has hadits polarization direction changed by exactly 90degrees. The strength of this received signal is muchsmaller than that recorded in either of the like-polarized modes, but still significantly larger thanthe signal from areas of surface scattering. Vegetatedareas thus appear brighter than nonvegetated areasin a cross-polarized radar image.

AIRSAR images of differentpolarization from Kilauea;compare to the C-Band VVimage and interpretive map onpage 9. The grass and forestareas are bright in the HVimage because volumescattering caused significantdepolarization of the returningsignal. Surface scattering bythe rougher lava-flows causedmuch less depolarization, sothese flow areas are darker inthe HV image, though not asdark as the smooth lava flows.Note that the rough lavas andvegation have nearly thesame brightness signatures inthe like-polarized images.

C-Band HH

C-Band HV


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Viewing Color Band CombinationsIn the future more radar systems will likely ac-quire images at several wavelengths with multiplepolarization modes in order to better discriminatedifferent types of surface materials. As with mul-tispectral optical images, these multiband radarimages can be used as components of an RGBraster (color) display. By carefully choosingwavelength/polarization components you can cre-ate color images that emphasize various surfaceproperties and enhance your ability to interpretsurface materials, as shown by the examples onthis page.

AIRSAR Image of the area of Kilauea shown onprevious pages. C-band HV is used as the redchannel, L-band HV as green, and L-band VVas blue. Forest appears in the bright blue-greencolors, grassland reddish-brown, rough lavaflows dark blue, and smooth lava flows black.

Image of north central Thailand from the SIR-Csensor, courtesy of NASA-JPL. This is a deeplydissected area of plateaus and hills. Forest areasappear green and agricultural areas andsettlements appear blue. (L-band HH = red, L-bandHV = green, and C-band HV = blue.)

Image of the Manaus region of Brazilfrom the Spaceborne Imaging Radar-Csensor operated on the NASA spaceshuttle in 1994, courtesy of NASA-JPL.Three L-band polarization channels wereused to create the color image: HH asred, HV as green, and VV as blue. Greenareas are heavily forested, smooth riversurfaces are blue, and flooded forestareas are yellow and red.

Image of central Sumatra from the SIR-C sensor, courtesy of NASA-JPL.Forest appears green in the image,while the dark to bright pink areas havebeen cleared for palm oil plantations.L-band polarization channel color as-signments are the same as in theManaus image above.


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Radar images have an inherently grainyappearance due to random brightness varia-tions that are most apparent in otherwiseuniform areas of an image. The cause ofthis characteristic image noise, calledspeckle, can be traced to the nature of theradar signal and its interactions with sur-face objects.

A radar imager emits pulses of energy witha single wavelength and frequency, whichremain constant during subsequent inter-actions. The return signal that creates animage cell comes from an area large enoughto contain numerous individual featuresthat scatter the radar energy. The signalrecorded from each of these areas repre-sents the aggregate of all of the signals fromthe individual scattering elements. Be-cause those scattering elements differ inposition and height, the travel paths of theircomponent return signals may vary inlength by fractions of a wavelength ormore. The strength of the overall recordedsignal then depends upon how well thepeaks in all of the sinusoidal wave formsare aligned. If the peaks are exactlyaligned, constructive interference occurs,producing a strong signal and a bright im-age cell. If the peaks in one wave line upwith the troughs of another, the two waveseffectively cancel each other out in the pro-cess called destructive interference. Theseinterference effects are random, and canvary dramatically with slight differences indirection and angle of view. As a result,the brightness of adjacent radar image cellsfluctuates randomly relative to the averagebrightness for that surface and viewinggeometry.

Radar Speckle Noise

Constructive Interference

Destructive Interference

ResultingSignal

Resulting Signal

Schematic Sample of a RadarImage of a Homogeneous Area to

Illustrate Speckle

Full constructiveinterference

Full destructiveinterference

Varying degreesof interference

Radar image (C band HH) of relativelyhomogeneous agricultural fields withvarying brightness, illustratingcharacteristic radar speckle noise.


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Multi-look Processing and SpeckleSpeckle can be treated mathematically as a separaterandom noise factor superimposed on the “true” re-turn radar signal. Fortunately this noise does notsignificantly alter the average signal strength in ho-mogenous areas represented by many adjacent imagecells. This means that speckle can be reduced byaveraging the values of cells in local neighborhoods,at the expense of degraded spatial resolution.

Speckle reduction by averaging occurs as part of astandard radar processing step that produces a multi-look image. In a raw Synthetic Aperture Radar(SAR) image, each line of cells in the range di-rection represents the image of one radar “look”,corresponding to a single transmitted radar pulse.(Each look is actually produced by sophisticatedprocessing of a number of consecutive pulses,however). The dimensions of these single-lookimage cells are not the same in the range andazimuth directions, because range and azimuthresolution are controlled by different system char-acteristics. Resolution in the azimuth directionin SAR systems is the same regardless of range,and is typically several times better than the rangeresolution. A single-look image cell is thereforeseveral times larger in the range direction thanin the azimuth direction. Multi-look processingaggregates small groups of adjacent range lines(usually three or four) into wider multi-look rangelines to produce cells that are more nearly square.The new cell values are computed by averagingin the azimuth direction (averaging cells with thesame range position). The resulting multi-lookimage has reduced speckle and better image ge-ometry. Because of these benefits, suppliers ofradar images routinely apply multi-look process-ing, and raw single-look images are only usedfor specialized purposes. Most digital radar im-ages that you encounter therefore will bemulti-look images.

Speckle obscures edges andother image details andmakes it difficult todetermine the typical radarresponse from uniform areasof a radar image.Processing radar images toreduce speckle cansignificantly improve theirinterpretive value.

Group of 4single-lookimage lines

Range D

irection (image lines)

Resultingmultilook

image line

Multi-look Processing


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Reducing Speckle Using Radar FiltersAlthough multi-look processing reduces radarspeckle, it does not eliminate it. The remainingspeckle noise can be further reduced by applying aspatial filter to the image. The TNTmips SpatialFilter process includes several radar filters tailoredto the particular characterisitics of radar speckle.

Unlike the additive noise found in many images, ra-dar speckle is approximately multiplicative. In otherwords, the range of the random brightness deviationsincreases with the average gray level of a local area.Using this mathematical model, a measure of thenoise range (standard deviation) can be estimatedfrom the actual brightness variations in the image,using either the local neighborhood of the filter win-dow or the entire image. The radar filters (Sigma,Frost, Lee, and Kuan) use these noise estimates invarious ways to control the filter process. The ob-jective is to reduce the speckle noise in uniformregions by some type of averaging while preservingthe brightness variations that occur at the boundariesbetween areas of differing overall brightness. Theradar filters come closer to meeting this objectivethan simple low-pass or median filters. Several ofthese radar filters have parameters that you can ad-just to optimize the results.

Application of the radar filters can reduce but noteliminate speckle noise. Several applications of oneor more filters may be required to reduce speckle toan acceptable level. However, each application of afilter results in some blurring, or loss of spatial de-tail. You will need to determine the balance betweennoise reduction and loss of spatial resolution that isappropriate for your radar images and interpretationobjectives.

STEPSchoose Image/ Filter /Spatial Filter from theTNTmips menuchoose Radar from theClass menu on theRaster Spatial Filteringwindowexamine the options onthe Type menu

Original radar image

After applying 3x3 Frost filter

After second application of3x3 Frost filter

Because the radar filters utilize image statistics, you shouldapply them prior to resampling operations, including theslant range to ground range transformation discussed next.


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Converting Slant Range to Ground RangeSTEPS

choose Image /Resample and Reproject/ Radar Slant to Groundfrom the TNTmips menu

Near Range Far Range

Slant-range image

Ground-rangeimage

A B C

A'

B'

C'

Slant-range display plane

Ground range (GR)

Slant range (SR)

Depression angle (θ)

Height (H

)

The near range por-tion of the imageappears highly com-pressed comparedto ground range.

Radar images are initially acquired as slant rangeimages that distort the relative positions of groundfeatures. Objects in a slant range image are posi-tioned in the range direction (along image lines)according to their direct-line distance from theantenna (slant range). As shown in the cross-section below, this is geometrically equivalentto projecting all ground positions along spheri-cal wavefronts to a display plane extending fromthe antenna to the farthest range position. Thescale of a slant range image varies in the rangedirection, with objects in the near range beingcompressed relative to those in the far range (seeequal-sized objects A, B, and C in the figure). Aslant range image can be converted to a groundrange presentation using the platform height (sen-sor altitude minus surface elevation), the slant rangevalue for each cell, and the cell size in range and

azimuth directions.

GR = H ( ( 1/ sin2θ ) - 1)½

The transformation to groundrange assumes a horizontalground surface and constantplatform height, and doesnot remove spatialdistortions related totopographic relief.

GR = ( SR2 - H2 )½

;

Depending on the level ofprocessing you select,radar images you acquirefrom a commercial sourcemay already be in groundrange presentation.


page 18

Terrain Distortions in Radar Imagesradar wavefronts arriving at:F = base of front slopeT = topR = base of rear slope

FT

RR

T = F TF

R

slant ra

nge to se

nsor

R R RF FT (T,F) Tground rangeimage

foreslope foreshortening layoverpoint image of foreslope

A color display of SIR-C radarbands covering a ruggedmountainous area in south-east Tibet. The look directionis toward the left side of theimage. The labels indicateseveral of the many examplesof foreshortening (F) and lay-over (L) of slopes facing thesensor.

F

look direction

A transmitted radar pulse can be modeled simply as an expanding sphericalwavefront that sweeps across the terrain to one side of the sensor flight path.Variations in the elevation of the terrain affect the relative travel times of the radarenergy that returns to the sensor, producing unique geometric distortions thatremain in radar images even after conversion to ground range.

Gentle slopes (those less steep than the local portion of the radar wavefront) thatface the sensor are imaged with foreshortening. The image of the top of a hill orridge is displaced toward the near range compared to its actual map position andrelative to the image of the base of the slope (see left side of illustration above). In

essence, the facing slope is imaged in less timethan the back slope (or a comparable area of flatterrain), which makes it appear that hills are leaningtoward the sensor. If the foreslope has the samesteepness as the radar wavefront (middle portionof illustration above), the entire slope is illuminatedat the same instant, and its radar image collapsesto a single bright cell in each image line.

If the foreslope is steeper than the radar wavefront(right side of above illustration), the top of theforeslope is actually imaged before the base,producing the extreme distortion called layover. Hillsdistorted by layover appear to have narrow, brightforeslopes. Layover is more severe in the near rangeportion of an image where the depression angle ishigher, because the slope of the radar wavefrontdecreases with increasing depression angle.

L


page 19

Sources of Radar ImageryRADARSAT: launched in 1995 by the CanadianSpace Agency to monitor environmental change(especially Arctic Ocean sea-ice conditions) and tosupport resource sustainability. RADARSAT orbitsat an altitude of 798 km with a 24-day revisit cycle.Its synthetic aperture radar (SAR) operates in C-band(5.7 cm) with HH polarization. The radar beam canbe shaped and steered to create different image swathwidths and resolutions. The standard beam modehas a 100 km swath width and 28-meter resolution.Data is distributed through RADARSATInternational.

ERS-1, ERS-2: operated by the European SpaceAgency (ESA), ERS-1 was launched in 1991 andERS-2 in 1995. Both satellites orbit at 785 kmaltitude and include a C-band SAR operating withVV polarization. The swath width is 100 km with30 meter resolution. The revisit cycle for eachsatellite is 16 to 18 days. The ERS radar sensorsoperate with a relatively steep depression angle (67degrees) to emphasize ocean surface features, butland terrain interpretation is hampered by severelayover effects.

JERS-1: launched by the National SpaceDevelopment Agency of Japan as a multipurposeenvironmental monitoring platform. The satelliteincluded an L-band SAR with HH polarization thatoperated from 1992 to 1998. The image swath was75 km with 18-meter resolution.

SIR-C/X-SAR: this sensor was carried on two flightsof the NASA Space Shuttle in 1994. Its antenna arrayoperated in X-band, C-band, and L-band withmultiple polarization modes and variable depressionangles. Swath widths are 50 to 100 km withresolution from 25 to 50 meters. Radar imagery wasacquired for research into applications in landscapeecology, environmental monitoring, hydrology,geomorphology, and volcanology.

MacDonald, Dettwiler &Associates Ltd.13800 Commerce ParkwayRichmond, BC V6V 2J3CANADATel: (604) 278-3411Fax: (604) 231-2796E-mail:[email protected]:www.mdacorporation.com

ERS-1 and ERS-2 radarimages are distributedcommercially by variousdealers worldwide.

Technical information isavailable at the ERSMissions Home Page:http://earth1.esrin.esa.it/

ERS/

Information on the SIR-Cmission is available at:http://southport.jpl.nasa.gov/

sir-c/

Data is distributed throughthe USGS:http://edcsns17.cr.usgs.gov/

sir-c/survey.html


page 20Voice: (402) 477-9554 email: [email protected]: (402) 477-9559 internet: www.microimages.com

MicroImages, Inc.11th Floor - Sharp Tower206 South 13th StreetLincoln, Nebraska 68508-2010 USA

MicroImages, Inc. publishes a complete line of professional software for advanced geospatialdata visualization, analysis, and publishing. Contact us or visit our web site for detailed productinformation.

TNTmips Pro TNTmips Pro is a professional system for fully integrated GIS, imageanalysis, CAD, TIN, desktop cartography, and geospatial database management.

TNTmips Basic TNTmips Basic is a low-cost version of TNTmips for small projects.

TNTmips Free TNTmips Free is a free version of TNTmips for students and profession-als with small projects. You can download TNTmips Free from MicroImages’ web site.

TNTedit TNTedit provides interactive tools to create, georeference, and edit vector, image,CAD, TIN, and relational database project materials in a wide variety of formats.

TNTview TNTview has the same powerful display features as TNTmips and is perfect forthose who do not need the technical processing and preparation features of TNTmips.

TNTatlas TNTatlas lets you publish and distribute your spatial project materials on CD orDVD at low cost. TNTatlas CDs/DVDs can be used on any popular computing platform.

Indexazimuth.......................................................5contrast.........................................8,9corner reflector......................................7depression angle.................................5,18dielectric constant..................................7ERS-1, ERS-2.....................................3,4,19filtering..........................................16foreshortening.................................18ground range..........................................17import...............................................4interference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14layover.............................................18multi-look image.......................................18polarization........................................2,11-13

Radarsat...............................................3,4,19range............................................................5resolution....................................................5roughness................................................6,10scattering.................................................6,11shadowing....................................................7SIR-C/X-SAR.........................14,19,20,23,24slant range.................................................17surface scattering.......................................11specular reflection.......................................6speckle noise........................................14-16synthetic aperture radar (SAR)................5,15volume scattering.......................................11wavelength......................................2,3,10,11

Advanced Software for Geospatial AnalysisRADAR

INTERPRET

Documents

Tutorial: Introduction to Interpreting Digital RADAR Images · in the tutorial booklets Displaying Geospatial Data and TNT Product Concepts. ... volcanic mudflow deposits ... Interpreting