Adaptive Channel Attention and Feature Super-Resolution for Remote Sensing Images Spatiotemporal Fusion

CNN-based Spatiotemporal image fusion (STIF) methods have achieved better performance than traditional researches. However, most CNN-based methods fail to make full use of hierarchical features, and ignore the quality and distribution characteristics of feature maps in fine-grained STIF. In this paper, we propose a network with channel attention and feature super-resolution for STIF (CAFSRNet). First, our method uses the low resolution time-domain changing images as input to extract changes more accurately and simplify computational overhead. Second, channel attention mechanism is introduced into Cross-spatial Resolution Mapping module to make the network pay more attention to informative features. Third, by adding feature super-resolution into the supervision process, we enhance the distribution of feature maps and the quality of mapping results. The qualitative and quantitative experimental results on various datasets demonstrate the superiority of our proposed method over the state-of-the-art methods.