RADIOMETRIC AND GEOMETRIC ERRORS

Course: Introduction to RS & DIP

Mirza Muhammad WaqarContact:

mirza.waqar@ist.edu.pk+92-21-34650765-79 EXT:2257

RG610

Outlines

Digital Image Advantages of Digital Image Constraints of Remote Sensing System Image Preprocessing Geometric Distortions Radiometric Distortions

Digital Image

A metric Cell Spatial information Spectral information

Satellite data mostly available in grid file format

Used for Spatial Analysis (Quantitative Analysis) Spectral Analysis (Qualitative Analysis)

For information extraction form satellite imagery, we normally perform both, qualitative as well as quantitative analysis.

Advantages of Digital Image

1. Flexible structure 2. All mathematical and statistical operations can

be applied3. Advance image processing packages are

available to process digital imagery.

Constraints of Remote Sensing Systems

Remote sensing systems are not yet perfect and contains four types of resolution constraints:

Spatial Spectral Temporal Radiometric

These constraints (plus complexity of land and water surfaces) cause errors in data/image acquisition process.

This leads to degradation of quality of remote sensing data/image. Before remote sensing data is analyzed, data/image needs to be

preprocessed to restore image quality. Image restoration involves correction of distortion, degradation, and

noise introduced during the imaging process.

Image Preprocessing

During image processing, anomalies are removed which can create problem during information extraction.

1. Spatial Anomalies (Geometric Distortions)2. Spectral Anomalies (Radiometric Distortions)

Geometric Distortions

There are two types of geometric distortions exists in satellite data

1. Systematic Errors2. Non-Systematic Errors

Systematic Errors

These errors are system dependent also called platform based errors.

If the quantity of error is know These errors can be removed

Mostly found in mechanical sensors

For example, velocity of Landsat scanners’ motor varies and its variation is known. A mathematical model can be develop to remove such

distortions.

Systematic Errors

1. Scan Skew Distortion2. Earth Rotation Effect3. Platform Velocity4. Mirror Scan Velocity5. Panoramic Distortions6. Perspective Distortions

Scan Skew Distortion

During the time the scan mirror completes one active scan, the satellite moves along the ground track.

Therefore, scanning is not at right angles to the satellite velocity vector (ground track) but is slightly skewed, which produce along track geometric distortion, if not corrected

Earth Rotation Effect

26-28 seconds required to capture a Landsat image.

In Landsat TM/ETM+, up till 16 scan line, distortion is gradual, however after 16 lines, distortion is greater

Satellites having small swath width have less earth rotation effect.

Earth Rotation

Earth Rotation Effect

Platform Velocity

Variation of pixel size in terms of information content. 1:100 => Less information => Large pixel size 1:10 => More information => Small pixel size

When information is increasing, pixel size is decreasing.

Platform Velocity

Image Scale Distortions

Size of the pixel is changing

For satellite, we have equal sampling rate

Due to Scale distortions, Dwell Time is changing

Information content varies

Information content is the indicator of scale

Mirror Scan Velocity

Mirror scan velocity of landsat scanner is not constant It is slower first then it increases

Perspective Distortions

As all remote sensing satellites exit at high altitude. Earth curvature effect become very prominent

which cause perspective distortions

This effect can be removed by rectification (we will

study in next lecture).

Distortion in Scale due to Scanning System

Distortion in Scale due to Scanning System

Non-Systematic Error

All the terminologies make for non-systematic distortions was developed for aerial platforms.

Two types of non-systematic distortions:1. Developed due to Altitude2. Developed due to Attitude

Altitude Distortions

Due to altitude variation, FOV and IFOV changes. Causing scale distortions.

Attitude Distortions

Geometric Distortions

Radiometric Error

Internal cause: When individual detectors do not function properly or are improperly calibrated.

External cause: Atmosphere (between the terrain and the sensor) can contribute to noise (i.e., atmospheric attenuation) such that energy recorded does not resemble that reflected/emitted by the terrain.

Radiometric Error

Internal Error Correction (Correction for Sensor System Detector Error) Element(s) ij:

may go bad at the beginning of the scan line (line-start problem) may go out of calibration or adjustment (line stripping or banding), or may drop out (line drop out) completely.

Detect: Take average of brightness value (BV) of surrounding pixels and compare to BVij.

Correct: Assign average BV if BVij is beyond a given threshold. Or, correct from overlapped images. Improves fidelity of brightness value magnitude. Improves visual

interpretability.

Radiometric Correction

External Error Correction (Correction for Environmental Attenuation Error) Two sources of environmental attenuation:

Atmospheric attenuation Topographic attenuation

Atmospheric attenuation (caused by scattering and atmosphere) Not a problem for most land-cover-related studies because signals from soil,

water, vegetation, and urban area may be strong and distinguishable. Problematic for biophysical information from water bodies (e.g., chlorophyll a,

suspended sediment, or temperature) or vegetated surfaces (e.g., biomass, NPP, % canopy closure) because there is only subtle difference in reflectance.

Error correction: data is calibrated with in situ measurements, and/or, data is corrected with a model atmosphere. Error minimized using multiple "looks" at the same object from different vantage

points or using multiple bands.

Radiometric Correction

Topographic attenuation Slope and aspect effects include shadowing of areas of interest. Goal of slope-aspect correction: To remove topographically induced illumination

variation (so that two objects having same reflectance show same BV even though they may have different slope and aspect).

Forest stand classification is improved when slope-aspect errors are corrected.

Correction is based on illumination (proportion of direct solar radiation hitting a pixel). Digital Elevation Model (DEM) required. DEM and remote sensing data must be geometrically registered and resampled to same spatial resolution.

Amount of illumination depends on relative orientation of pixels toward the sun.

Questions & Discussion