SAMCE-CR: SAR-Guided Alignment and Multifrequency Collaborative Enhancement for Cloud Removal in Remote Sensing Images Fusion

Abstract

In global Earth observation, multispectral imaging is frequently hindered by extensive cloud cover, leading to observation gaps. Introducing synthetic aperture radar (SAR) data with all-weather penetration capability for multimodal cloud removal has become a key approach to achieving global spatiotemporal seamless remote sensing monitoring. Existing SAR-optical fusion methods often lack effective decoupling and differentiated processing of heterogeneous data. This leads to texture distortion in reconstructed images, limiting the accuracy of downstream interpretation and target recognition. Therefore, a cloud removal method based on SAR-guided alignment and multifrequency collaborative enhancement is proposed in this article. First, the spatial geometric features of SAR images are mined by multireceptive field gating mechanism, and robust structural priors are extracted for under-cloud ground object reconstruction while effectively suppressing coherent speckle noise; Second, with the help of the deformable alignment module, the geometric alignment of SAR and optical images is realized with reference to optical images, alleviating the problem of scale and spatial misalignment; Finally, through the multifrequency collaborative enhancement module, the high and low frequency information are adaptively separated, and an improved attention mechanism is adopted to enhance the high and low frequency information, respectively, which effectively suppresses cloud interference and maintains surface details. Results on the M3R-CR and LuojiaSET-OSFCR datasets show that the proposed method comprehensively outperforms the other eight compared methods. Compared with the suboptimal method, the peak signal-to-noise ratios (PSNRs) achieved by the proposed method on the two datasets are improved by 0.5643 and 0.1964 dB, respectively. The source code of SAMCE-CR is shared at https://github.com/RSIDEA-ECUT/SAMCE-CR