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Map-Matching Techniques For Use With Multispectral/Multitemporal Data

Conventional scene matching algorithms such as mean absolute difference or normalized cross correlation are often ineffective for use with images collected at different times or in different spectral bands because of the occurrence of significant differences in scene reflectivity or emissivity. A new scene registration concept has been devised for the dissimilar image problem and tested against real-world data with encouraging results. Whereas conventional approaches to map-matching are based upon the computation of a measure of image similarity, with a penalty imposed for non-corresponding intensities, the proposed algorithm provides a reward for region correspondence as evidenced by clustering of the image intensity joint histogram. This approach results in a maximum match score for images which differ by a random permutation of intensity levels. The use of an intensity-free matching algorithm provides the designer with the flexibility to use either sensor derived references, or synthetic references which are region coded, material coded, or coded with the predicted sensor response.