Cloud Detection Method Using CNN Based on Cascaded Feature Attention and Channel Attention

Abstract

Cloud detection is of great significance for the subsequent analysis and application of remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In this article, we propose a cloud detection method using convolutional neural networks based on cascaded feature attention and channel attention (CFCA-Net). The CFCA-Net uses cascaded feature attention module (CFAM) to enhance the attention of the network toward important color feature and texture feature. The CFAM cascaded the color feature attention and texture feature attention module in the encoder. The CFAN-Net also uses channel attention to highlight the important information in the channel dimensions. The attention module is based on multi-scale features and uses dilated convolution with different dilation rates to obtain information about multiple receptive fields. Moreover, a loss function combined quadtree and binary cross-entropy (BCE) was also introduced to make the network focus on the edge of cloud area. We validated our CFCA-Net on the Gaofen-1 wide field-of-view (WFV) imagery dataset. The experimental results show that the CFCA-Net performs well under different scenarios, and its overall accuracy reaches 97.55%. Moreover, subjective cloud detection results also prove the effectiveness of our algorithm.