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IMCORR Software

How IMCORR Works

IMCORR takes two images and a series of input parameters and attempts to match small subscenes (called 'chips') from the two images. The program uses a fast fourier transform - based version of a normalized cross-covariance method (see Berenstein, 1983). The most common use of this type of algorithm in image processing is to accurately locate tie-point pairs in two images to coregister them. However, if the images are already coregistered by other means, the algorithm may be used to find the displacements of moving features, provided that the features show little change in their appearance, and that the motion is strictly translational. IMCORR takes as input the image names and sizes, parameters determining search chip size, reference chip size, grid spacing, and output filename. Further, preset offsets of search chip centers may be specified, and subareas of the full image files may be used to restrict the area over which IMCORR attempts to find displacements. At each of the gridpoints IMCORR calculates a correlation index for every location at which the reference chip will entirely fit within the search chip. IMCORR takes the correlation values in the vicinity of the best integer-pixel match and interpolates a peak correlation location to sub-pixel precision. The program returns a file containing the locations of the grid centers for the reference chips, the displacements required to best match the chip pairs (or indicates that none could be found), and several quality control parameters that may be used to evaluate the validity of the match. We use this program to measure glaicer velocities; however, the same program may be useful for other applications.

The correlation, peak finding, and error estimation routines in IMCORR are derived from FORTAN subroutines from the Land Analysis System software (LAS) written at NASA Goddard Space Flight Center and USGS Eros Data Center. IMCORR consists of a C code wrapper which makes the use of the routines more straightforward and automated for velocity-mapping applications.

A paper describing the details of this technique and the preprocessing of images required to optimize the displacement measurements is published in Remote Sensing of Environment (Scambos et al., 1992; see IMCORR bibliography). We will gladly provide a preprint upon request.


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Loading The Software

Download the tar file for the IMCORR distribution:

prompt>uncompress imcorr-rel1.1.tar.Z


prompt>tar xvf imcorr-rel1.1.tar

or you can simply say:

gzip -dc imcorr-rel1.1.tar.{Z or gz} | tar xvf -

(untarring this file will create an Imcorr directory)

[NOTE: Release 1.1 differs (very) slightly from 1.0. Please see the included README.txt file for the latest documentation. (It's more up-to-date than this web page.)]

This directory is roughly 1.25 Mbytes. The IMCORR source code is roughly 75 kilobytes. It requires both C and FORTRAN compilers. It contains no graphics commands in either language, and should be relatively easy to port to a variety of systems. We use a Silicon Graphics Indigo2 workstation; the software has also been compiled on a SUN Sparcstation. We have included makefile files for both SGI and SUN workstations. To generate an executable file from the source code, type:

prompt>make imcorr (SGI version)

or

prompt>make -f makefile.SUN imcorr (SUN version) (See the file makefile.SUN.note.txt if you have trouble with the SUN makefile as is.)

or

prompt>make -f makefile.Linux imcorr (Linux version)

On any system, to remove the object files, type:

prompt>make clean

All of these makefiles produce an executable file named imcorr.


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Input Parameters

IMCORR version 1.0 takes either 5, 10, or 14 input parameters, which are typed on the same line as the 'imcorr' command separated by spaces. Simply typing 'imcorr' and return gives a list of the required input parameters for each of these three formats.

List of Input Parameters:

The reference image will have the smaller, 'reference chip', subimages derived from it, at regular grid spacings, and these chips will be compared to larger 'search chip' subimages derived from the search image. The images must be the same size, and a single (sample, line) coordinate system will be used for both of them. In the following discussion, it is assumed that the images are coregistered, i.e., that a feature that does not move would be found at the same (sample, line) coordinates in both images. Regarding maximum image size, machine memory is dynamically allocated during an IMCORR run. Therefore, images may be very large; however, larger images will result in slower processing due to memory swapping.

Large search and reference chip sizes slow the runtime considerably, and a large reference chip may cause problems if any distortion of the features occurs between the two images. Note that if the search and reference chips are derived from the same (sample, line) grid centers in their respective images (the default case), the maximum displacement of a feature that can be measured is: (search chip size/2 - reference chip size/2) * C2, and this maximum applies only to exactly diagonal motion.

Grid spacing parameter determines the density of attempted matches, and therefore also has a strong effect on runtime. Note that reducing the grid spacing parameter by a factor of 2 increases the number of points attempted by 4. The grid of attempted matches will be more or less centered within the reference image area; since search chips and reference chips are derived from the same (sample, line) centers in the default case, reference chips are derived from centers no closer than (search chip size/2) from the reference image edge. In the search image, matches may be found as close as (reference chip size/2) from the search image edge.

For a variety of reasons, it is often desirable to give a preset offset of the search chip center relative to the reference chip center. For example, in cases where the displacement is approximately known beforehand, one may reduce the size of the search chip if a preset offset of the search chip area in the approximate direction of the displacement is entered. This is the purpose of the next two parameters. The offsets are in image coordinates, relative to the SEARCH chip; therefore, entering a positive value for the x, or sample, offset coordinate will place the center of the reference chip to the right of the search chip (by moving the search chip center leftward). A positive value for the y, or line, offset will place the center of the reference chip down, or in the positive line direction.

A subarea of the full images may be specified, limiting the search for displacement matches to a specific region within the scenes. The run proceeds as if the subareas were the full image size. However, the output locations of any matches are given relative to full scene coordinates.


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Output Parameters

The output file is in ASCII format, and consists of a series of lines with nine entries each. A line is generated for every gridpoint, whether a successful match was found or not, and no matter what the quality of the match was.

List of Output Parameters:

The first two parameters give the location of the center of the reference chip. The total displacement is exactly the distance formula applied to the x and y displacements listed later; it is zero if no successful match was found. The correlation strength parameter is a function of the character of a correlation index 'map' that the greycorr subroutines create and evaluate - it is a combination of peak height of the correlation map, height of peak to second-highest peak, and height of peak to background value of correlation index.

Formula

correl. strength = (peak correl. value - mean background value)/ std.dev. of background values + (peak correl. value - highest value more than 3 pixels away from peak)/ std.dev. of background values + 0.2 * (number of "large" values more than 3 pixels away from peak - 1.0)

Its magnitude changes somewhat with the input parameters for any given run - larger search and reference chips tend to produce larger strength of correlation values for a given region of the images. The result flag parameter is an integer returned by imcorr indicating if a good match was found, or if not, what went wrong. Flag values mean:

Case 2 is indicated if the match was found within 2 pixels of the limit of where the reference chip can fit within the search chip - such proximity to the edge does not allow for good statistical determination of whether the match is valid. If case 2, 3, or 4 occurs, zero values are output for peak strength, displacements, and errors.

The next two output parameters are the offsets required to best match the reference chip pixels with the search chip pixels, again in image coordinates (positive y is downward). Preset offsets in the input parameters are included in the reported displacement measurement. The x and y error estimates are derived from a peak-height-to-peak-width comparison. The values are sensitive to the size of the reference chip; good matches with larger reference chips yeild smaller x and y errors, in general.


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Running a Test Analysis

Within the same directory as the IMCORR software there are four images that have been preprocessed for IMCORR analysis, and a series of results files labeled test1.out, test2.out, and test3.out. The images, called conv.y87, conv.y89, fang.y87, and fang.y89, are Landsat TM subscenes of a portion of Ice Stream D in West Antarctica (roughly 81! 20' S, 135! W). They are byte images 512 x 512 pixels in size. The image pairs are already coregistered relative to basement features and are high-pass filtered to enhance small, sharp ice surface features that move with the ice's surface velocity. See the preprocessing notes below for more information on preparing images for IMCORR. In the sample runs suggested below, we will use these images as input for IMCORR.

Example 1

The following is an example of an IMCORR run over an area where a high percentage of the matches are 'correct'. It uses all the default parameters.

prompt>imcorr conv.y87 conv.y89 512 512 test1.out

you will see the following as the run proceeds:

opening conv.y87 as ref image and conv.y89 as search image, size 512 by 512
writing to test1.out
xoff=0 yoff=0 xext=512 yext=512
opening conv.y87 size 512 by 512
reading conv.y87
done reading image
read 262144 pixels
opening conv.y89 size 512 by 512
reading conv.y89
done reading image
read 262144 pixels
l 64 r 64 t 64 b 64

64 64 8.582 3.793 1 0.753 8.549 0.093 0.065
64 89 0.000 0.000 3 0.000 0.000 0.000 0.000
64 114 9.233 6.515 1 1.842 9.048 0.589 0.045
64 139 9.785 5.911 1 0.713 9.759 2.193 0.128
.
.
.
439 439 29.983 11.734 1 -13.120 21.260 0.157 0.197

The output beginning with the first 64, 64 line will go into a file labeled test1.out. This data may then be edited in a number of ways, using the strength parameter, result flag, x and y errors, etc., to remove bad or poor quality points.

Example 2

Another example of a IMCORR run, using the fang image pair :

prompt>imcorr fang.y87 fang.y89 512 512 test2.out 128 32 20 0 0

opening fang.y87 as ref image and fang.y89 as search image, size 512 by 512
writing to test2.out
xoff=0 yoff=0 xext=512 yext=512
opening fang.y87 size 512 by 512
reading fang.y87
done reading image
read 262144 pixels
opening fang.y89 size 512 by 512
reading fang.y89
done reading image
read 262144 pixels
l 64 r 64 t 64 b 64

64 64 0.000 0.000 3 0.000 0.000 0.000 0.000
64 84 23.411 7.392 1 -11.579 20.347 4.308 4.425
64 104 0.000 0.000 3 0.000 0.000 0.000 0.000
64 124 23.283 10.332 1 -10.797 20.628 0.388 0.219
.
.
.
444 444 5.912 116.630 1 -3.613 4.680 8.011 4.871

This area is more difficult for a number of reasons. You will note that a diagonal region (corresponding with a chaotically fractured zone in the image) has no successful matches. This is due to high shear in this area. Further, there are some areas where relatively stationary shadowing has caused matches which are displaced relative to the probable direction of flow. This area would require careful editing, using the quality parameters written in the output file, as well as hand editing by inspection.

Example 3

An example of a run which attempts to get data in the shear area is

prompt>imcorr fang.y87 fang.y89 512 512 test3.out 32 16 8 5 -11 50 0 200 512

This run has a reduced reference chip size, thus encompassing less velocity gradient, and uses a reduced search chip area and offsets to select a smaller, more likely target area. A denser grid spacing gives more chances that usuable matches will be found. It also uses the subimaging input parameters to limit the region over which this parameter set will be applied (over most of the image, this parameter set will yield poorer results than the first - only in the high-strain region will it be better).

The results on the screen from the second run should be:

opening fang.y87 as ref image and fang.y89 as search image, size 512 by 512
writing to test3.out
xoff=50 yoff=0 xext=200 yext=512
opening fang.y87 size 512 by 512
reading fang.y87
done reading image
read 102400 pixels
opening fang.y89size 512 by 512
reading fang.y89
done reading image
read 102400 pixels
l 24 r 8 t 8 b 34

71 8 0.000 0.000 2 0.000 0.000 0.000 0.000
71 16 0.000 0.000 3 0.000 0.000 0.000 0.000
71 24 0.000 0.000 3 0.000 0.000 0.000 0.000
71 32 0.000 0.000 3 0.000 0.000 0.000 0.000
.
.
.
239 480 0.000 0.000 2 0.000 0.000 0.000 0.000

(other lines had successful matches)

The above 'style' of running IMCORR should be used with caution, as it presumes that a specific, narrow, range of displacements is occurring in the analyzed area.


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Preprocessing of Imagery

Since most users of this software will be concerned with the measurement of ice velocity using digital satellite imagery, or, perhaps, digitized aerial or satellite photographs, this section will address preprocessing from that standpoint.

The objective of the preprocessing is to generate images of the surface features of the ice, with as little noise, sensor effects, and solar illumination effects as possible. To have the surface features appear as similar as possible in the sequential imagery, the imagery should be taken at the same time of day, and if possible, at same time of year. With multiband data, such as Landsat, a good first step is to generate a first principal component image of the visible and near-infrared bands. This will greatly reduce the noise and has the effect of giving greater brightness resolution to the image (Orheim and Lucchitta, 1987 see IMCORR bibliography). Scan-line striping and swathing, if present, should be removed, as itscan-line striping and swathing, if present, should be removed, as its presence will tend to generate matches based on the stripes and not the features. One technique which should be applicable to a number of striping problems is discussed in Crippen, 1989 (see IMCORR bibliography). Sun angle variations across the image may be addressed by dividing the pixel brightness values by the cosine of the sun elevation as it varies across the image area, or by simply high-pass filtering. High-pass filtering may also be used to remove bed-related topographic features which remain stationary as the ice flows over them. The presence of such features will distort the displacements measured because the IMCORR routine will attempt to shift matches based on the surficial features so that the larger-scale pixel brighness variations associated with the bed topography also tend to match.


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IMCORR Bibliography

Bernstein, R. (1983), Image geometry and rectification, In Manual of Remote Sensing (R. N. Colwell, ed.), American Society of Photogrammetry, Falls Church, VA, pp.881-884.

Crippen, R. E. (1989), A simple filtering routine for the cosmetic removal of scan-line noise from Landsat TM P-tape imagery, Photogrammetric Engineering and Remote Sensing, 55, 327-331.

Fahnestock, M. A., Scambos, T.A., and Bindschadler, R. A.,1992 Semi-automated ice velocity determination from satellite imagery, Eos, 73, 493.

Orheim, O., and Lucchitta, B. K. (1987), Snow and ice studies by Thematic Mapper and Multispectral Scanner Landsat images, Annals of Glaciology, 9, 109-118.

Scambos, T. A., M. J. Dutkiewicz, J. C. Wilson, and R. A. Bindschadler, 1992. Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sensing Environ., 42(3), 177-186.


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$Date: 2006-02-20 17:02:56-07 $