A Coarse-to-Fine Scene Matching Method for High-Resolution Multiview SAR Images

Scene matching involves establishing correspondences between multiple images of the same location and poses significant challenges for synthetic aperture radar (SAR) images due to the anisotropic scattering prosperities of SAR targets; variations in looking and azimuth angles further complicate the matching process. A matching algorithm is proposed based on a coarse-to-fine framework to address these issues. First, a coarse matching employing normalized cross correlation (NCC) with a sliding window is applied to filter out irrelevant regions, reducing distractions, and shortening the processing time. Subsequently, a Siamese neural network (SNN), incorporating ResNet-50 and convolutional block attention module (CBAM) for enhanced feature extraction, is introduced to learn and discern differences between inputs. The effectiveness and robustness of the proposed method are validated through extensive experiments using a self-made dataset derived from Umbra Satellite.