Local Affine Preservation With Motion Consistency for Feature Matching of Remote Sensing Images

As a fundamental and essential task in the field of remote sensing and photogrammetry, feature matching endeavors to establish reliable correspondences between two sets of feature points extracted from an image pair of the same scene. In this article, we propose an efficient and general algorithm, which is called local affine preservation (LAP) matching, for robust feature matching of remote sensing images. We start by constructing the putative point correspondences according to the similarity of well-designed feature descriptors and then focus on removing false matches from the putative set. The key idea of LAP is to search motion-consistent neighborhoods and maintain the local neighborhood topological structures of the true putative matches. To this end, we present a local geometric constraint, which exploits the property of affine invariance to measure the preservation degree of neighborhood topology, since the property is still held under both rigid and complex nonrigid transformations for a minimum topological unit. Moreover, in order to avoid the random distribution of outliers to destroy the neighborhood structure preservation of inliers, a neighbor mining strategy is introduced to search motion-consistent neighbors for each correspondence. We formulate the problem into an optimization model and derive a closed-form solution with linearithmic time complexity. Extensive experimental results on remote sensing images demonstrate that our LAP is able to achieve better performance over the current state-of-the-art approaches.

Automated summary

This article introduces an efficient and general algorithm called local affine preservation (LAP) matching for robust feature matching of remote sensing images. By constructing putative point correspondences and focusing on removing false matches, the algorithm aims to maintain the local neighborhood topological structures of true putative matches, achieving better performance over current state-of-the-art approaches.