Diffeomorphic matching with multiscale kernels based on sparse parameterization for cross-view target detection

We present a novel, robust target detection method to locate a target from a reference image (UAV image) according to a target image (remote sensing satellite image). Using sparse parameterization diffeomorphic matching based on multiscale kernels, the approach modeling the nonrigid transformation function between the reference image and target image is proposed to complete target detection. Furthermore, it designs an feature point matching fusing intensity and phase information to determine the corresponding keypoints, which solves the cross-view problem. Then, the displacements of the corresponding keypoint sets are classified into several subsets using the probabilistic mixture model. The sparse parameterization diffeomorphic matching is executed in the subsets, removing the influence of outliers in the corresponding keypoints. The subset with the maximum evaluation for the transformation is utilized to locate the target. Finally, multiscale kernels based on sparse parameterization are integrated into diffeomorphic matching, solving the large deformation problems between target and reference images. The proposed approach incorporates the stationary velocity field into the diffeomorphism and utilizes the Lie group idea for the stationary velocity to trade off the matching accuracy and computational time. On the University-1652 image dataset with multi-view and multisource properties, experimental results show that the proposed approach is robust to noise and large deformations.