Robust Feature Matching Using Motion Consistency and Geometrical Constraint for UAV Images

Establishing feature correspondence between unmanned aerial vehicle (UAV) images is a fundamental task in photogrammetry and remote sensing. However, existing methods still suffer from noisy matches due to the cluttered ground objects presented in UAV images. In this paper, we proposed a novel feature matching method combining motion smoothness and geometrical constraint for UAV images. Given a pair of UAV images (a left one and a right one), once the putative matches were generated, we first divide the left image into a certain number of non-overlapping regions. Then, for local features in each region of the left image, we find their corresponding matching points in the right image, and perform a DBSCAN [1] to cluster the found points into groups. Finally, we determine the group (of points) in the right image which preserves the motion consistency with the region (of points) in the left image, and satisfy a local polar coordinate system-based geometric constraint, as the matching group of that region. Extensive experiments conducted on a set of different conditioned UAV image pairs, show that the proposed method can achieve good performance in terms of precision and recall, outperforming those comparison methods.