用雷达和带云图像合成清晰光学影像，提升遥感数据可用性
High-Quality Cloud-Free Optical Image Synthesis Using Multi-Temporal SAR and Contaminated Optical Data
- 设计新型网络结构与融合注意力机制增强细节重建
- 在多个指标上超越现有方法，PSNR达26.978，SSIM达0.648
- 构建真实场景数据集TCSEN12，支持复杂云况下图像合成研究
克服云遮挡和卫星重访周期长导致的数据缺失，对支撑遥感应用至关重要。本文针对复杂云况下的光学数据缺失问题，提出CRSynthNet图像合成网络，引入下行上行模块（DownUp Block）与融合注意力机制，显著提升重建精度。实验验证其在结构细节恢复、光谱一致性保持和视觉效果方面均优于对比方法，定量指标表现优异：峰值信噪比（PSNR）达26.978，结构相似性指数（SSIM）为0.648，均方根误差（RMSE）为0.050。同时，本研究构建了TCSEN12数据集，专为云遮挡下的光学图像补全任务设计，包含云覆盖图像，并利用早期影像预测后期影像，更贴近真实应用场景。该研究提供了实用方法与宝贵资源，推动光学遥感图像合成发展。
原文摘要 · Abstract (English)
Addressing gaps caused by cloud cover and the long revisit cycle of satellites is vital for providing essential data to support remote sensing applications. This paper tackles the challenges of missing optical data synthesis, particularly in complex scenarios with cloud cover. We propose CRSynthNet, a novel image synthesis network that incorporates innovative designed modules such as the DownUp Block and Fusion Attention to enhance accuracy. Experimental results validate the effectiveness of CRSynthNet, demonstrating substantial improvements in restoring structural details, preserving spectral consist, and achieving superior visual effects that far exceed those produced by comparison methods. It achieves quantitative improvements across multiple metrics: a peak signal-to-noise ratio (PSNR) of 26.978, a structural similarity index measure (SSIM) of 0.648, and a root mean square error (RMSE) of 0.050. Furthermore, this study creates the TCSEN12 dataset, a valuable resource specifically designed to address cloud cover challenges in missing optical data synthesis study. The dataset uniquely includes cloud-covered images and leverages earlier image to predict later image, offering a realistic representation of real-world scenarios. This study offer practical method and valuable resources for optical satellite image synthesis task.
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