Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
It addresses cloud occlusion issues for applications like environmental monitoring and disaster response, representing an incremental improvement over existing methods.
This research tackled the problem of cloud contamination in optical satellite imagery by developing a Cloud-Attentive Reconstruction Framework that integrates SAR-optical feature fusion and deep learning-based reconstruction to generate cloud-free images, achieving a PSNR of 31.01 dB, SSIM of 0.918, and MAE of 0.017.
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.