coreg

This program co-regsisters two images using a constant sample/line translation. This implies of course that the internal geometry of the both images be nearly the same so that the translation can be computed. That is, this program will not work if the translation varies significantly throughout the image. However, there are still useful capabilities in this program if this condition is not meet. If the condition of near constant translation is met then the translation can be computed to sub-pixel accuracy. The basic principle behind this program is to compute an average translation by computing local translations spaced evenly throughout the image. The number and spacing of local translations is user defined. This allows for many output options including 1) directly creating the translated image, 2) creating a control network which can be used in other programs (e.g., qnet, warp), especially if the translation is not constant across the image, or 3) creating a flat-field file usable in spreadsheets or plotting packages to visualize magnitude and direction of varying translations.

NOTE: This program can utilize many different techniques for computing the translation. It is recommend that you read "Automatic Registration in Isis 3.0". It is essential for under understanding how to create a "registration definition" file and how to size your search and pattern chips. We will continue with the discussion of functionality of this program assuming the reader has a fundamental knowledge of Automatic Registration. For the brave we give an example of a defintion file (REGDEF):

         Object = AutoRegistration
           Group = Algorithm
             Name         = MaximumCorrelation
             Tolerance    = 0.7
           EndGroup

           Group = PatternChip
             Samples = 20
             Lines   = 20
             ValidMinimum = 1400
             ValidPercent = 75
           EndGroup

           Group = SearchChip
             Samples = 90
             Lines   = 90
             ValidMinimum = 1400
             ValidPercent = 75
           EndGroup
         EndObject
     

You will provide two input cubes, one which will be translated (FROM) and the other is held fixed (MATCH). The images must have the same number of samples and lines and can have only one band (use cube attributes if necessary). A sparse grid will be defined across the held image using the parameters ROWS and COLUMNS. If the user does not provide values for the sparse grid size it will be automatically computed as follows: COLUMNS = (image samples - 1) / search chip samples + 1. Similarly for ROWS. Conceptually, the sparse grid defined by ROWS and COLUMNS will be laid on top of both images with even spacing between the rows (or columns) and but no row will touch the top or bottom of the image. That is, the grid is internal to the image.

At each grid intersection we will compute a translation. This is done by centering the search chip at the grid intersection for the image to be translated (FROM) and centering the pattern chip at the grid intersection for the held image (MATCH). The pattern chip is walked through the search chip to find the best registration (if any). Again the details of how this is done is described in the document "Automatic Registation in Isis 3.0". The local translation is recorded at all grid intersections that had a successful registration. It will be written to the control network and/or flat-file if requested. The average of the local translations is then used to compute an overall sub-pixel translation which can be applied to the output image (TO).

Some tips to improve odds of a successful registration. In general don't go too small with the pattern chip size, 20x20 is probably a good starting point. Also, the larger the translation, the larger the search chip size will need to be. So if your translation is only a couple of pixels keep the search chip slightly bigger than the pattern (e.g., 25x25 vs 20x20). However if the translation is large you will need to expand the seach area. For example, if the translation is roughly 45 pixels and your pattern is 20x20 the search area should be 20+2*45 or 110x110.

This example shows the coreg application. The rows and columns parameters are left as default.

Sample,Line,TranslatedSample,TranslatedLine,SampleDifference,LineDifference,GoodnessOfFit
211.875,164.283,200,133,-11.8751,-31.2829,0.991597
211.766,429.437,200,398,-11.7661,-31.4369,0.995987
        

The above text file is the ffile.txt file created when coreg is ran. The flat file is comma seperated and can easily be imported into excel.

coreg from=./lunar1.cub match=./lunar2.cub t=out.cub flatfile=ffile.txt

First Input image	First Input image for coreg Parameter Name: FROM This is the 800 by 800 input image to be translated for the coreg example.
Second Input image	Second Input image for coreg Parameter Name: MATCH This is the 800 by 800 input image to be held for the coreg example.

Output image showing results of the coreg application.

Output image for coreg Parameter Name: TO This is the 800 by 800 output image that results.