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Improved SIFT match for optical satellite images registration by size classification of blob-like structures

Due to high rate of false match and expensive computation cost, the existing scale-invariant feature transform (SIFT) operators are not efficient to register two optical satellite images that have a great spatial resolution difference. Some scale restriction schemes were proposed to reduce the false match of SIFT keypoints and computational cost. However, it is observed that many keypoints are still not correctly matched. This problem often leads to the failure of automatic registration in industrial applications. To solve this problem, the images being registered are normalized to adjust the scale of blob-like structures and to preclude useless blob-like structures. Then blob-like structures are classified according to their physical sizes, and keypoint matching is restricted to matching for the blob-like structures having the same physical sizes. The scale normalization and size classification significantly improve the correct match rate as well as computational cost. Experiments on two pairs of satellite images demonstrate the effectiveness of the proposed method.