CGSNet: Channel Group Shuffling Network for Remote Sensing Image Fusion

High-resolution multi-spectral (HRMS) images have been widely used in various fields, however, they can not be directly obtained due to the physical hardware limits of existing remote sensing satellites. Therefore, the pansharpening technique has been widely explored as an effective tool to generate HRMS images by fusing the complementary information of low-resolution multi-spectral (LRMS) images and high-resolution panchromatic (PAN) images. Existing deep learning-based pansharpening methods mainly focus on enhancing the spatial representation ability of the network, while paying little attention to modeling spectral dependencies in spite of its significance for remote sensing data. In this paper, we propose a simple yet effective channel group shuffling (CGS) module to explore the implicit relationships with regard to the adjacent and cross-channels while considering spatial information. To be specific, the proposed CGS module consists of two components: the channel group module and the feature shuffle fusion module. The former enhances the diversity of spectral information and cross-channel information communications while ensuring the spectral order of the input feature. The latter integrates the cross-group feature maps with rich spatial-spectral information. Equipped with the proposed functional module, our image fusion network, dubbed CGSNet, produces favorable results against existing state-of-the-art counterparts over various satellite datasets. Ablation studies further verify the flexibility and effectiveness of our core design.