Multi-spectral remote sensing image registration via spatial relationship analysis on SIFT keypoints
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Multi-sensor image registration is a challenging task in remote sensing. Considering the fact that multi-sensor devices capture the images at different times, multi-spectral image registration is necessary for data fusion of the images. Several conventional methods for image registration suffer from poor performance due to their sensitivity to scale and intensity variation. The scale invariant feature transform (SIFT) is widely used for image registration and object recognition to address these problems. However, directly applying SIFT to remote sensing image registration often results in a very large number of feature points or keypoints but a small number of matching points with a high false alarm rate. We argue that this is due to the fact that spatial information is not considered during the SIFT-based matching process. This paper proposes a method to improve SIFT-based matching by taking advantage of neighborhood information. The proposed method generates more correct matching points as the relative structure in different remote sensing images are almost static.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium
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