Transcript Radiometric and Geometric Errors

RADIOMETRIC AND GEOMETRIC ERRORS
Mirza Muhammad Waqar
Contact:
[email protected]
+92-21-34650765-79 EXT:2257
RG610
Course: Introduction to RS & DIP
Outlines
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Digital Image
Advantages of Digital Image
Constraints of Remote Sensing System
Image Preprocessing
Geometric Distortions
Radiometric Distortions
Digital Image
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A metric Cell
Spatial information
 Spectral information
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Satellite data mostly available in grid file format
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Used for
Spatial Analysis (Quantitative Analysis)
 Spectral Analysis (Qualitative Analysis)
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For information extraction form satellite imagery,
we normally perform both, qualitative as well as
quantitative analysis.
Advantages of Digital Image
1.
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Flexible structure
All mathematical and statistical operations can
be applied
Advance image processing packages are
available to process digital imagery.
Constraints of Remote Sensing Systems
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Remote sensing systems are not yet perfect and contains four types of
resolution constraints:
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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
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During image processing, anomalies are
removed which can create problem during
information extraction.
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2.
Spatial Anomalies (Geometric Distortions)
Spectral Anomalies (Radiometric Distortions)
Geometric Distortions
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There are two types of geometric distortions
exists in satellite data
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2.
Systematic Errors
Non-Systematic Errors
Systematic Errors
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These errors are system dependent also called
platform based errors.
If the quantity of error is know
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These errors can be removed
Mostly found in mechanical sensors
For example, velocity of Landsat scanners’ motor
varies and its variation is known.
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A mathematical model can be develop to remove such
distortions.
Systematic Errors
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Scan Skew Distortion
Earth Rotation Effect
Platform Velocity
Mirror Scan Velocity
Panoramic Distortions
Perspective Distortions
Scan Skew Distortion
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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
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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
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Variation of pixel size in terms of information
content.
 1:100
=> Less information => Large pixel size
 1:10 => More information => Small pixel size
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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
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Mirror scan velocity of landsat scanner is not
constant
 It
is slower first then it increases
Perspective Distortions
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As all remote sensing satellites exit
altitude.
at high
 Earth
curvature effect become very prominent
which cause perspective distortions
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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
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All the terminologies make for non-systematic
distortions was developed for aerial platforms.
Two types of non-systematic distortions:
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2.
Developed due to Altitude
Developed due to Attitude
Altitude Distortions
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Due to altitude variation, FOV and IFOV changes.
Causing scale distortions.
Attitude Distortions
Geometric Distortions
Radiometric Error
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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
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Internal Error Correction (Correction for Sensor System
Detector Error) Element(s) ij:
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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
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External Error Correction (Correction for Environmental Attenuation
Error)
Two sources of environmental attenuation:
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Atmospheric attenuation
Topographic attenuation
Atmospheric attenuation (caused by scattering and atmosphere)
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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:
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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
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Topographic attenuation
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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