An Effective Network Integrating Residual Learning and Channel Attention Mechanism for Thin Cloud Removal

Abstract

Cloud-contaminated images seriously disturb the effective information in ground observations and reduce the availability of remote sensing images. This letter presents a thin cloud removal method for cloud-contaminated images based on a residual channel attention network. Compared with the present thin cloud removal methods not showing different attention to cloud components and ground details, the proposed method introduces channel attention mechanism into the residual learning (RL) path, which achieves suppressing thin clouds and enhancing the details of ground scenes simultaneously. Based on this, both residual channel attention module (RCAM) and residual group (RG) are constructed, playing the role of basic modules to stack an encoder–decoder-based residual channel attention network, which effectively avoids the loss of ground information in the deep layers. Compared with recent state-of-the-art (SOTA) methods, experimental results on both actual and synthesized cloudy images show the proposed method’s superiority in reconstructing rich details of ground scenes.