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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.

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A 3D solid model-aided object cueing method that matches phase angles of directional derivative vectors at image pixels to phase angles of vectors normal to projected model edges is described. It is intended for finding specific types of objects at arbitrary position and orientation in overhead images, independent of spatial resolution, obliqueness, acquisition conditions, and type of imaging sensor. It is shown that the phase similarity measure can be efficiently evaluated over all combinations of model position and orientation using the FFT. The highest degree of similarity over all model orientations is captured in a match surface of similarity values vs. model position. Unambiguous peaks in this surface are sorted in descending order of similarity value, and the small image thumbnails that contain them are presented to human analysts for inspection in sorted order.