Thick Clouds Removal from Multitemporal ZY-3 Satellite Images Using Deep Learning

Abstract

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.