A Robust Point-Matching Algorithm for Remote Sensing Image Registration

Feature point matching is a critical step in feature-based image registration. In this letter, a highly robust feature-point-matching algorithm is proposed, which is based on the feature point descriptor calculated by the triangle-area representation (TAR) of the K nearest neighbors (KNN-TAR). The affine invariant descriptor KNN-TAR is used to find the candidate outliers, and then, the real outliers will be removed by the local structure and global information. The experimental results show that the proposed method can remove the outliers from the initial matching result even when the outliers are of high proportion. Compared with graph transformation matching and restricted spatial-order constraints, KNN-TAR outperforms these methods with higher stability and precision.