Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions

2025/06/22 by Bui, Trong-An, Le, Thanh-Thoai
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.17885

Abstract

Cloud contamination significantly impairs the usability of optical satellite imagery, affecting critical applications such as environmental monitoring, disaster response, and land-use analysis. This research presents a Cloud-Attentive Reconstruction Framework that integrates SAR-optical feature fusion with deep learning-based image reconstruction to generate cloud-free optical imagery. The proposed framework employs an attention-driven feature fusion mechanism to align complementary structural information from Synthetic Aperture Radar (SAR) with spectral characteristics from optical data. Furthermore, a cloud-aware model update strategy introduces adaptive loss weighting to prioritize cloud-occluded regions, enhancing reconstruction accuracy. Experimental results demonstrate that the proposed method outperforms existing approaches, achieving a PSNR of 31.01 dB, SSIM of 0.918, and MAE of 0.017. These outcomes highlight the framework's effectiveness in producing high-fidelity, spatially and spectrally consistent cloud-free optical images.

Citations

• UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
• GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
• NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function
• On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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• Cloud removal in remote sensing images using generative adversarial\n networks and SAR-to-optical image translation