SAR-Assisted Cloud Removal With Similarity Guided Fusion and Multiscale Representation

Cloud removal is essential for improving the quality of optical remote sensing images in various downstream applications. Leveraging auxiliary synthetic aperture radar (SAR) data with cloud penetrating ability can accurately reconstruct details obscured by thick cloud cover. However, the presence of complex cloud types in optical images hampers the effective use of the similar geometrical characteristics shared with SAR images, resulting in spectral distortion and fuzzy textures. In this paper, we propose a similarity-guided fusion SAR-assisted cloud removal network. The unidirectional multimodal fusion module effectively utilizes similarity to recover structures and fill in spectral information. Additionally, the bidirectional multiscale reconstruction module refines and aggregates these multiscale intermediate reconstruction results in terms of semantic guidance and detail preservation. The experimental results on the SEN12MS-CR and GF23-CR datasets demonstrate that our method achieves superior quantitative and visual reconstruction. The source code is available at https://github.com/phaeton2017/SGFCR.