The presence of clouds greatly reduces the ground information of high-resolution satellite data. In order to improve the utilization of high-resolution satellite data, this article presents a cloud removal method based on deep learning. This is the first end-to-end architecture that has great potential to detect and remove clouds from high-resolution satellite data. For cloud detection, a convolution neural network (CNN) architecture is used to detect them. For cloud removal, the content generation network, the texture generation network, and the spectrum generation network based on traditional CNN are proposed. The proposed CNN architecture can use multisource data (content, texture, and spectral) as an input of the unified framework. The results of both the simulated and real image experiments demonstrate that the proposed method is robust and can effectively remove thick clouds, thin clouds, and cloud shadows. In addition, compared with some existing methods, the proposed method can recover land cover information accurately.

中文翻译：

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使用深度学习从多时相 ZY-3 卫星图像中去除厚云

云的存在大大减少了高分辨率卫星数据的地面信息。为了提高高分辨率卫星数据的利用率，本文提出了一种基于深度学习的去云方法。这是第一个具有从高分辨率卫星数据中检测和去除云层的巨大潜力的端到端架构。对于云检测，使用卷积神经网络 (CNN) 架构来检测它们。对于去云，提出了基于传统CNN的内容生成网络、纹理生成网络和频谱生成网络。所提出的 CNN 架构可以使用多源数据（内容、纹理和光谱）作为统一框架的输入。模拟和真实图像实验结果表明，该方法具有鲁棒性，可以有效去除厚云、薄云和云阴影。此外，与现有的一些方法相比，所提出的方法可以准确地恢复土地覆盖信息。