The background in image of remote sensing is often complicated and changeable, and the edge of cloud and its shadow is irregular. In the traditional method, the bright part of the background is easy to be misjudged as cloud, while the dark part is easy to be misjudged as cloud shadow. Moreover, the edge information of the extracted cloud and its shadow is rough, and it is easy to miss the judgment for the thin cloud part and the light cloud shadow part. In order to solve the above problems, a strip pooling channel spatial attention network is proposed. In this work, the strip pooling residual network is used as the backbone network to obtain the feature of cloud and its shadow. The strip pooling residual network can obtain more accurate local position information of cloud and its shadow, which can improve the accuracy of edge segmentation. Channel attention and spatial attention combine shallow spatial information with deep context information, so that cloud and its shadow can be accurately segmented from the background. The experimental results demonstrate that method in our work can acquire more accurate segmentation edge than existing methods, hence it is practical in accurate cloud and its shadow segmentation.

遥感影像背景往往复杂多变，云的边缘及其阴影不规则。在传统的方法中，背景的亮部分容易被误判为云，而暗的部分容易被误判为云影。而且提取的云及其阴影的边缘信息比较粗糙，容易漏判薄云部分和浅云阴影部分。为了解决上述问题，提出了条带池化通道空间注意力网络。在这项工作中，条带池化残差网络作为骨干网络来获取云及其阴影的特征。条带池化残差网络可以获得更准确的云及其阴影的局部位置信息，可以提高边缘分割的准确性。通道注意力和空间注意力将浅层空间信息与深层上下文信息相结合，从而可以从背景中准确地分割出云及其阴影。实验结果表明，我们工作中的方法比现有方法可以获得更准确的分割边缘，因此在准确的云及其阴影分割中具有实用性。