Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images

As a fundamental and important task in many applications of remote sensing and photogrammetry, feature matching tries to seek correspondences between the two feature sets extracted from an image pair of the same object or scene. This paper focuses on eliminating mismatches from a set of putative feature correspondences constructed according to the similarity of existing well-designed feature descriptors. Considering the stable local topological relationship of the potential true correspondences, we propose a simple yet efficient method named multiscale Top $K$ Rank Preservation (mTopKRP) for robust feature matching. To this end, we first search the $K$ -nearest neighbors of each feature point and generate a ranking list accordingly. Then we design a metric based on the weighted Spearman’s footrule distance to describe the similarity of two ranking lists specifically for the matching problem. We build a mathematical optimization model and derive its closed-form solution, enabling our method to establish reliable correspondences in linearithmic time complexity, which requires only tens of milliseconds to handle over 1000 putative matches. We also introduce a multiscale strategy for neighborhood construction, which increases the robustness of our method and can deal with different types of degradation, even when the image pair suffers from a large scale change, rotation, nonrigid deformation, or a large number of mismatches. Extensive experiments on several representative remote sensing image data sets demonstrate the superiority of our method over state of the art.