Image registration using a kernel partial least squares based mismatches removal method

Feature point matching is a critical step in feature based image registration. For remote sensing images, scale invariant feature transform (SIFT) feature matching is often affected by similar descriptors and there are mismatches. To improve the quality of feature matching and image registration, we propose to use spatial relationship along with the SIFT descriptor for registration. Firstly, initial matches are obtained based on distances between SIFT feature descriptors. Secondly, the spatial relationship of matched points is encoded by kernel partial least squares (KPLS). By analyzing the collinearity of the KPLS features from coarse to fine, false matches are indicated. Finally, correct matches are used to realize accurate registration. Experimental results show an overall significant reduction of the mismatches while maintaining a high rate of correct matches. Compared with several other feature matching methods, the proposed method provides comparable or better results.