Transcript Radiometric and geometric correction

Remote sensing image correction
Introductory readings – remote sensing
http://www.microimages.com/documentation/Tutorials/introrse.pdf
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
Preprocessing
Radiometric 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 layers
 enable mosaicking and comparison
Radiometric correction: modification of DNs
Errors
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
Errors: Sensor Failure & Calibration
Sensor 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 2003
http://landsat.usgs.gov/products_slc_off_data_information.php
SLC compensates for forward
motion of the scanner during scan
Atmospheric Interference: clouds
clouds 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].
Geometric Correction
Corrected image scene orientation ‘map’
Uncorrected data ‘path’
Pixels and rows
Group discussion
• Why is rectification needed for remote sensing images?
Why is rectification needed
Raw 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 distortions
In 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)
Geocorrection
Rectification – assigning coordinates to (~6) known locations - GCPs
GCP = Ground Control Point
Resampling -
resetting the pixels (rows and columns) to match the GCPs
Rectification
Data 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
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 Interpolation
New 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
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf
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
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
Striping from projecting
SRTM data, from
Lat/long to UTM; Chile
Now for something completely different – perfect registration needed….
100% Marilyn Monroe
->
100% Margaret Thatcher