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

TL;DR

This paper introduces a novel deep learning framework that fuses SAR and optical satellite data with attention mechanisms to effectively reconstruct cloud-free optical images, improving usability for environmental and disaster applications.

Contribution

It presents a new cloud-aware fusion and reconstruction framework that combines SAR-optical features with adaptive loss weighting for superior cloud removal in satellite imagery.

Findings

Achieved PSNR of 31.01 dB, SSIM of 0.918, and MAE of 0.017.

Outperformed existing methods in cloud-free optical image reconstruction.

Demonstrated high fidelity and spectral consistency in reconstructed images.

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.