Supervised Alignment Perception and Modality Difference Selection Enhanced SAR-Optical Fusion for Cloud Removal

Leveraging synthetic aperture radar (SAR) data to assist in cloud removal demonstrates promising potential and has achieved satisfactory results in restoring clear contours of images. However, there are three critical challenges: inherent geometric discrepancies between SAR and optical images complicate cross-modal correlation modeling; modality-specific noises lead to radiometric inconsistency and texture loss in cross-modal information aggregation; and diverse clouds and complex scenes impede the attainment of both detailed inpainting and global consistency. Therefore, we propose a novel network integrating supervised alignment perception strategy (SAPS) and modality difference selection module (MDSM) to enhance SAR-optical fusion for cloud removal. Specifically, the SAPS consists of supervised training and an alignment perception module (APM) to address spatial misalignments and enhance the stability of deviation perception. Then, the MDSM dynamically focuses on different regions and eliminates modality-specific noise through mutual dynamic guidance. Furthermore, the locality-enhanced Mamba (LEM) module in reconstruction optimization enhances the consistency of the overall reconstruction results and the expressiveness of local details. Experiments conducted on the SEN12MS-CR, LuojiaSET-OSFCR, and simulated WHU-OPT-SAR datasets assess reconstruction performance from multiple perspectives. The proposed method not only achieves superiority in basic quantitative and qualitative validation but also demonstrates advantages in reconstruction under different cloud coverage levels and across various terrains.