Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow

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.