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Radiometric and Geometric Errors

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Presentation on theme: "Radiometric and Geometric Errors"— Presentation transcript:

1 Radiometric and Geometric Errors
Mirza Muhammad Waqar Contact: EXT:2257 RG610 Course: Introduction to RS & DIP

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

3 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.

4 Advantages of Digital Image
Flexible structure All mathematical and statistical operations can be applied Advance image processing packages are available to process digital imagery.

5 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.

6 Image Preprocessing During image processing, anomalies are removed which can create problem during information extraction. Spatial Anomalies (Geometric Distortions) Spectral Anomalies (Radiometric Distortions)

7 Geometric Distortions
There are two types of geometric distortions exists in satellite data Systematic Errors Non-Systematic Errors

8 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.

9 Systematic Errors Scan Skew Distortion Earth Rotation Effect
Platform Velocity Mirror Scan Velocity Panoramic Distortions Perspective Distortions

10 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

11 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.

12 Earth Rotation

13 Earth Rotation Effect

14 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.

15 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

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

17 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).

18 Distortion in Scale due to Scanning System

19 Distortion in Scale due to Scanning System

20 Non-Systematic Error All the terminologies make for non-systematic distortions was developed for aerial platforms. Two types of non-systematic distortions: Developed due to Altitude Developed due to Attitude

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

22 Attitude Distortions

23 Geometric Distortions

24 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.

25 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.

26 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.

27 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.

28 Questions & Discussion

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