A Discriminative Point Matching Algorithm Based on Local Structure Consensus Constraint

Due to the existence of repetitive patterns, ambiguous features, and similar local structures in remote sensing images, it is inevitable that the outliers with local pseudoisomorphic structures are preserved as inliers, which makes point matching still a challenging problem. To improve the accuracy of feature matching, a discriminative point matching algorithm named local structure consensus constraint is proposed to remove the outliers from putative correspondences and find two local structure consensus graphs composed of inliers. First, a local structure descriptor is proposed to evaluate the corresponding structure similarity of the
$K$
-nearest neighbors. Then, a cost function is defined to evaluate the local structure consistency. With a two-stage outlier removing strategy, the feature points with different local structures are eliminated, and two local structure consensus graphs are obtained. To evaluate the performance of the proposed algorithm, 45 aerial image pairs taken around the Shandong Peninsula with repetitive local patterns and ambiguous features are used. Compared with five state-of-the-art point matching methods, the proposed algorithm is proven to be more accurate and efficient.