New UAV image registration method based on geometric constrained belief propagation

Image registration is a crucial step in the field of computer vision. However, the traditional scale invariant feature transform (SIFT) based method often suffers from many mismatches when being utilized to register unmanned aerial vehicle (UAV) images with obvious rotation, viewpoint change and similar textures. In order to tackle this problem, we formulate the image matching problem as a Markov Random Field (MRF) energy minimization problem and propose an accurate and fast image registration framework. First, a SIFT algorithm is utilized to extract keypoints and an algorithm called "Center of Mass" (CoM) is exploited to assign a single orientation for every point instead of SIFT since SIFT may cause ambiguity in subsequent process; second, based on the local adjacent spatial relationship between a feature point and its neighboring points, a new concept of local spatial constraints is proposed to characterize the geometric consistency between them and incorporated into a belief propagation algorithm to obtain initial matching results; finally the residual mismatches are eliminated by the Random Sample Consensus (RANSAC) algorithm, meanwhile the transformation parameters are estimated. Experiment results demonstrate that the proposed framework can significantly enhance the registration performance in UAV image registration.