Shared contents alignment across multiple granularities for robust SAR-optical image matching

The matching of SAR and optical images is crucial for various remote sensing applications, such as monitoring natural disasters and change detection. However, the significant differences in geometric and radiometric properties between these two sensors pose challenges for robust and accurate matching. Recent deep learning-based approaches mitigate modality differences by aligning all contents on a single pixel-level feature map, leading to limited robustness to content differences and resolution variations. In this paper, we propose a novel robust SAR-optical matching network to address these challenges. To enhance robustness against noise and resolution changes, we align and match on feature maps of multiple granularities simultaneously. Further, we introduce the novel multi-granularity matching strategy called “look closer to match better” to reduce the computational burden of global matching across multiple granularities. This strategy employs coarse-grained features to quickly narrow down the search range, followed by the use of finer-grained features to gradually locate the finer matching position within a reduced search range, improving both efficiency and performance. Additionally, we address the issue of treating all regions equally during feature alignment by proposing a Non-Shared Contents Filtering (NSCF) module. This module adaptively filters out non-shared regions that are difficult to align, thereby avoiding its interference with the similarity measure of the consistent representation and enhancing robustness to content differences. We evaluate our framework on various satellite datasets. Experiments show that our method achieves the best performance on the SEN1-2 dataset and competitive generalization ability on other unseen satellite datasets.