remote sensing image correction - evtekusers.evtek.fi/~erkkir/imagetechnology2013/1...
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Remote sensing image correction
Introductory readings – remote sensing
http://www.microimages.com/documentation/Tutorials/introrse.pdf
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Preprocessing
Digital Image Processing of satellite images can be divided into:
Pre-processing
Enhancement and Transformations
Classification and Feature extraction
Preprocessing consists of: radiometric correction and geometric correction
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PreprocessingRadiometric Correction: removal of sensor or atmospheric 'noise', to more accurately represent ground conditions - improve image‘fidelity’:
correct data loss
remove haze
enable mosaicking and comparison
Geometric correction: conversion of data to ground coordinates by removal of distortions from sensor geometry
enable mapping relative to data layersenable mosaicking and comparison
Radiometric correction: modification of DNs
Errors
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Radiometric correction
Radiometric correction is used to modify DN values to account for noise, i.e. contributions to the DN that are a result of…
a. the intervening atmosphere
b. the sun-sensor geometry
c. the sensor itself – errors and gaps
Radiometric correction
We may need to correct for the following reasons:
a. Variations within an image (speckle or striping)
b. between adjacent / overlapping images (for mosaicing)
c. between bands (for some multispectral techniques)
d. between image dates (temporal data) and sensors
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Errors: Sensor Failure & CalibrationSensor problems show as striping or missing lines of data: Missing data due to sensor failure results in a line of DN values -every 16th line for TM data .. As there are 16 sensors for each band, scanning 16 lines at a time (or 6th line for MSS).
MSS 6 line banding – raw scan
MSS 6 line banding - georectified
TM data – 16 line banding
Sample DNs – shaded DNs are higher
Landsat ETM+ scan line corrector (SLC) – failed May 31 2003http://landsat.usgs.gov/products_slc_off_data_information.php
SLC compensates for forward motion of the scanner during scan
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Atmospheric Interference - haze
http://geology.wlu.edu/harbor/geol260/lecture_notes/Notes_rs_haze.html
Lower wavelengths are subject to haze, which falsely increases the DN value. The simplest method is known as dark object subtraction which assumes there is a pixel with a DN of 0 (if there were no haze), e.g. deep water in near infra-red. An integer value is subtracted from all DNs so that this pixel becomes 0.
Atmospheric Interference: cloudsclouds affect all visible and IR bands, hiding features twice: once with the cloud, once with its shadow. We CANNOT eliminate clouds, although we might be able to assemble cloud-free parts of several overlapping scenes (if illumination is similar), and correct for cloud shadows (advanced).
[Only in the microwave, can energy penetrate through clouds].
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Advanced slide: Reflectance to Radiance Conversion
DN reflectance values can be converted to absolute radiance values.
This is useful when comparing the actual reflectance from different sensors e.g. TM and SPOT, or TM versus ETM (Landsat 5 versus 7)
DN = aL + b where a= gain and b =n offset
The radiance value (L) can be calculated as: L = [Lmax - Lmin]*DN/255 + Lmin
where Lmax and Lmin are known from the sensor calibration.
This will create 32 bit (decimal) values.
Geometric CorrectionCorrected image scene orientation ‘map’ Uncorrected data ‘path’
Pixels and rows
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Why is rectification neededRaw remote sensing data contain distortions preventing overlay with map layers, comparison between image scenes, and with no geographic coordinates
To provide georeferencing
To compare/overlay multiple images
To merge with map layers
To mosaic images
e.g. google maps / google earth
*** Much imagery now comes already rectified … YEAH !!
Image distortionsIn air photos, errors include:
topographic and radial displacement;
airplane tip, tilt and swing (roll, pitch and yaw).
These are less in satellite data due to altitude and stability.
The main source of geometric error in satellite data is satellite path orientation (non-polar)
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Sources of geometric error (main ones in bold)
a. Systematic distortions
Scan skew: ground swath is not normal to the polar axis – along with the forward motion of the platform during mirror sweep
Mirror-scan Velocity and panoramic distortion: along-scan distortion (pixels at edge are slightly larger). This would be greater for off-nadir sensors.
Earth rotation: earth rotates during scanning (offset of rows).... (122 pixels per Landsat scene)
b. Non-systematic distortions
Topography: requires a DEM, otherwise ~ 6 pixel offset in mountainsCorrecting with a DEM involves ‘orthorectification’
Altitude and attitude variations in satellite: these are minor
Geocorrection
Rectification – assigning coordinates to (~6) known locations - GCPs
GCP = Ground Control Point
Resampling - resetting the pixels (rows and columns) to match the GCPs
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RectificationData pixels must be related to ground locations, e.g. in UTM coordinates
Two main methods:
- Image to image (to a geocorrected image) .... to an uncorrected image would be 'registration' not rectification
-Image to vectors (to a digital file)....
(black arrows point to known locations- coordinates from vectors or images)
Ortho-rectification = this process (since ~2000) enables the use of a DEM to also take into account the topography
Resampling methods
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf
New DN values are assigned in 3 ways
a.Nearest Neighbour Pixel in new grid gets the value of closest pixel from old grid –retains original DNs
b. Bilinear InterpolationNew pixel gets a value from the weighted average of 4 (2 x 2) nearest pixels; smoother but ‘synthetic’
c. Cubic Convolution(smoothest)New pixel DNs are computed from weighting 16 (4 x 4) surrounding DNs
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Resampling – pixel size
Previously during resampling stage, pixels were rounded to match UTM grid and DEMs:
Landsat MSS 80m raw pixels -> 50m corrected pixels
Landsat TM 30 (28.5) m -> 25m
BC TRIM DEM was built to 25m to match Landsat TM data
New millenium software can handle layers with different resolution, so downloaded TM scenes are mostly 30m pixels
Resampling
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf
Good rectification is required for image registration – no ‘movement between images
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Canadian Arctic mosaic
See also google maps, lrdw.ca/imap etc..
Northern Land Cover of Canada –
Circa 2000
http://ccrs.nrcan.gc.ca/optical/landcover2000_e.php
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Projections and reprojection
Global data might be downloaded as geographic (lat/long) or UTM zone
BC data as UTM or BC Albers
GIS and DIP software can display different projections ‘on the fly’
…but require reprojection for analysis and data overlay
Reprojecting vectors simply reassigns coordinates to points
Reprojecting rasters involves resampling every pixel (using nearest neighbour, bilinear or cubic convolution)
Release of new ASTER Global DEM (GDEM v2) – 3 Oct 2011
http://www.nasa.gov/topics/earth/features/aster20111017.html
Available in Geographic (Lat/Long) or UTM zone
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Ellipsoids and DatumsData will also have a datum:
NAD27: North American datum 1927NAD83: North American Datum 1983
There is a 100-200 metre difference between NAD27 and NAD83
NADCON83: NAD for continental USANAD83 Canada: based on Canadian landmassWGS84: World Geodetic System 1984
There is ‘very little’ difference between WGS84 and NAD83(flavours)
But ………………….. AIEEEEEEEEEE !
Reprojection – error stripes
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Reprojection – geographic (WGS84) to UTM / Albers
Striping from projecting SRTM data, from Lat/long to UTM; Chile