In this paper, an efficient technique to perform automatic registration and fusion for large misalignment remote sensing
images is proposed. It complements SIFT features with Harris-affine features, and uses the ratio of the first and second
nearest neighbor distance to setup the initial correspondences, then uses the affine invariant of Mahalanobis distance to
remove the mismatched feature points. From this correspondence of the points, the affine matrix between two different
images can be determined. All points in the sensed image are mapped to the reference using the estimated transformation
matrix and the corresponding gray levels are assigned by re-sampling the image in the sensed image. Finally, we develop
Burt's match and saliency metric and use neighborhood space frequency to fuse the registrated reference and sensed
remote sensing images in NSCT domain. Experiments on remote sensing images with large misalignment demonstrate
the superb performance of the algorithm.
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