Registrating Oblique SAR Images Based on Complementary Integrated Filtering and Multilevel Matching

This paper presents a novel registration method for oblique synthetic aperture radar (SAR) images based on complementary integrated filtering (CIF) and multilevel matching. Our algorithm is divided into three steps. First, we considered different type of noises and employed the CIF to increase the signal-to-noise ratio of SAR images. Second, complementary affine invariant features, namely maximally stable extremal regions and Harris-affine features, were extracted simultaneously from image pairs, and then the initial matches were obtained based on the scale invariant feature transform (SIFT) descriptor and Euclidean distance. Therefore, the fundamental and homography matrixes could be calculated between image pairs, and then more matches were obtained under quasi-affine invariant SIFT (QAISIFT) and the hybrid geometric constraints. We further implemented the least square matching (LSM) based on the second-polynomial geometric model (SPGM), and thus the matching error of each corresponding point can be compensated according to the optimal SPGM. Third, the precise registration was achieved based on the matches of the second step. Experiments on four groups of oblique SAR images demonstrated the effectiveness of the proposed method. The contribution of this paper includes three aspects. One is that the proposed CIF can remove SAR image noise as much as possible; another is that the proposed QAISIFT can achieve near affine invariance across viewpoint change images; the third is that the advanced LSM can notably improve the accuracy of feature matches.