Point-matching algorithm based on local neighborhood information for remote sensing image registration

Abstract

Robust feature point matching is a critical procedure in feature-based remote sensing image registration. A point-matching algorithm is proposed, which uses the similarity of local neighborhood information of point features. First, we establish a set of initial correspondences. Then we focus on removing incorrect correspondences (outliers) by local neighborhood information and increasing the number of correct correspondences (inliers). Finally, a global structure constraint is constructed for each remaining correct correspondence to increase the number of inliers and raise the correct rate simultaneously. Experimental results compared with three state-of-the-art methods illustrate that the proposed method can find more correct matching points with high accuracy.