用雷达和光学数据融合，生成无云的卫星图像。
Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
- 通过注意力机制融合雷达与光学图像特征
- 重建图像在云遮挡区域精度更高，PSNR达31.01 dB
- 适合遥感、环境监测等需要清晰图像的领域
云层遮挡严重降低光学卫星影像的可用性，影响环境监测、灾害响应和土地利用分析等关键应用。本文提出一种云感知重建框架，结合SAR与光学特征融合及基于深度学习的图像重建技术，生成无云光学影像。该框架采用注意力驱动的特征融合机制，对齐合成孔径雷达（SAR）的结构信息与光学数据的光谱特征。同时，引入云感知模型更新策略，通过自适应损失权重优先优化云遮挡区域，提升重建精度。实验结果表明，该方法优于现有技术，达到PSNR 31.01 dB、SSIM 0.918、MAE 0.017。结果验证了该框架在生成高保真、空间与光谱一致的无云光学图像方面的有效性。
原文摘要 · Abstract (English)
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.
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