MGFEI-Net: Multiscale Grouping Feedback Embedded Integrated Network for Panchromatic, Multispectral, and Hyperspectral Image Fusion

The spaceborne hyperspectral (HS) imagery with fine spectral information has broad application aspects; however, the low spatial resolution (LR) has limited the potential application values. Over the past few decades, a general strategy to improve the spatial resolution of the HS is to fuse the LR HS with an auxiliary moderate spatial resolution (MR) multispectral (MS) or a high spatial resolution (HR) panchromatic (PAN) image. However, most of the existing methods mainly focus on two-sensor fusion with the LR HS and MR MS images (i.e., MS–HS fusion) or the LR HS and HR PAN images (i.e., PAN–HS fusion). How to comprehensively combine the complementary spatial and spectral advantages of the LR HS, MR MS, and HR PAN observations, to obtain the optimal high-fidelity HR HS image is interesting and challenging. In this article, we propose a multiscale grouping feedback embedded integrated fusion network (MGFEI-Net) for the LR HS, MR MS, and HR PAN images. Specifically, an attention-based hybrid-scale integrated module (HIM) is designed by considering the spatial scale diversity of the HR PAN, MR MS, and LR HS images. Moreover, a multiscale grouping feedback embedded (MGFE) module with a top-to-bottom manner is proposed to capture more useful spatial–spectral features. Experiments were performed on the simulated and real datasets. Moreover, the robustness of the proposed PAN–MS–HS fusion under different large spatial resolution ratios (such as 8, 16, 32, and 64) was analyzed. The experimental results demonstrated the competitive performance of the proposed method.