SAR Image Registration Based on SAR-SIFT and Template Matching

Accurate image registration is essential for synthetic aperture radar (SAR) applications such as change detection, image fusion, and deformation monitoring. However, SAR image registration faces challenges including speckle noise, low-texture regions, and the geometric transformation caused by topographic relief due to side-looking radar imaging. To address these issues, this paper proposes a novel two-stage registration method, consisting of pre-registration and fine registration. In the pre-registration stage, the scale-invariant feature transform for the synthetic aperture radar (SAR-SIFT) algorithm is integrated into an iterative optimization framework to eliminate large-scale geometric discrepancies, ensuring a coarse but reliable initial alignment. In the fine registration stage, a novel similarity measure is introduced by combining frequency-domain phase congruency and spatial-domain gradient features, which enhances the robustness and accuracy of template matching, especially in edge-rich regions. For the topographic relief in the SAR images, an adaptive local stretching transformation strategy is proposed to correct the undulating areas. Experiments on five pairs of SAR images containing flat and undulating regions show that the proposed method achieves initial alignment errors below 10 pixels and final registration errors below 1 pixel. Compared with other methods, our approach obtains more correct matching pairs (up to 100+ per image pair), higher registration precision, and improved robustness under complex terrains. These results validate the accuracy and effectiveness of the proposed registration framework.