Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing missing regions. Nevertheless, the reconstruction of high-fidelity cloud-free images based on SAR-optical data fusion remains challenging due to differences in imaging mechanisms and the considerable contamination from speckle noise inherent in SAR imagery. To solve the aforementioned challenges, this paper presents a novel hybrid dynamic residual self-attention network (HDRSA-Net), aiming to fully exploit the potential of SAR images in reconstructing missing regions. The proposed HDRSA-Net comprises multiple dynamic interaction residual (DIR) groups organized into an end-to-end trainable deep hierarchical stacked architecture. Specifically, the omni-dimensional dynamic local exploration (ODDLE) module and the sparse global context aggregation (SGCA) module are used to form a local–global feature adaptive extraction and implicit enhancement. A multi-task cooperative optimization loss function is designed to ensure that the results exhibit high spectral fidelity and coherent spatial structures. Additionally, this paper releases a large dataset that can comprehensively evaluate the reconstruction quality under different cloud coverages and various types of ground cover, providing a solid foundation for restoring satisfactory sensory effects and reliable semantic application value. In comparison to the current representative algorithms, the presented approach exhibits effectiveness and advancement in reconstructing missing regions with stability. The project is accessible at: https://github.com/RSIIPAC/LuojiaSET-OSFCR.

中文翻译：

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HDRSA-Net： 用于 SAR 辅助光学图像云和阴影去除的混合动态残差自注意力网络


云和阴影通常会污染光学遥感图像，从而导致信息缺失。因此，对地球表面的连续时空监测需要有效地去除云和阴影。与光学卫星不同，合成孔径雷达 （SAR） 在所有天气条件下都具有主动成像能力，为重建缺失区域提供有价值的补充信息。然而，由于成像机制的差异以及 SAR 图像固有的散斑噪声的大量污染，基于 SAR 光学数据融合的高保真无云图像重建仍然具有挑战性。针对上述挑战，该文提出了一种新的混合动态残差自注意力网络 （HDRSA-Net），旨在充分利用 SAR 图像在重建缺失区域方面的潜力。所提出的 HDRSA-Net 包括多个动态交互残差 （DIR） 组，这些组被组织成一个端到端的可训练的深度分层堆栈架构。具体来说，采用全维动态局部探索 （ODDLE） 模块和稀疏全局上下文聚合 （SGCA） 模块形成局部-全局特征自适应提取和隐式增强。设计了多任务协同优化损失函数，以确保结果表现出高频谱保真度和相干的空间结构。此外，本文还发布了一个大型数据集，可以综合评估不同云层覆盖和各种类型地被物下的重建质量，为恢复令人满意的感官效果和可靠的语义应用价值提供了坚实的基础。 与目前的代表性算法相比，所提出的方法在稳定地重建缺失区域方面表现出有效性和进步性。该项目的访问地址为：https://github.com/RSIIPAC/LuojiaSET-OSFCR。