Rectified Neighborhood Construction for Robust Feature Matching With Heavy Outliers

Abstract

This letter is concerned with constructing reliable neighborhoods for the local consistency-based feature matching methods. To alleviate the impact of outliers on neighborhood construction, we propose a rectified neighborhood construction strategy (RNC), which can effectively enlarge the distribution between inliers and outliers. Besides, we also integrate an adaptive parameter estimation into the aforementioned rectified strategy, and it can contribute to determining a reasonable parameter for the rectified strategy. Finally, the experimental results on two representative remote sensing image data sets show that the proposed method can achieve satisfactory feature matching results compared with some state-of-the-arts.