Spatial–Spectral Cross Mamba Network for Hyperspectral and Multispectral Image Fusion

Currently, hyperspectral and multispectral image fusion methods based on local and global feature learning (e.g., CNN and Transformer) have achieved promising results. However, as the core part of transformer, the computational cost of the self-attention is quadratic with the image size, which severely limits its practical application. In this paper, we propose a spatial-spectral cross mamba network (SSCM) for hyperspectral and multispectral image fusion. By using the mamba structure, our model is able to obtain long-range spatial-spectral information with less computational complexity in comparison with the transformer structure. Specifically, we introduce a spatial-spectral cross mamba block to facilitate the interaction between hyperspectral and multispectral features, effectively enhancing the spatial-spectral feature representation ability of the network. In addition, a cross-scale spatial-spectral learning module based on the U-shaped structure is proposed to effectively extract the long-range high-frequency feature information at different scales. Extensive experimental results demonstrate that our method achieves comparable performance in comparison with some state-of-the-art image fusion methods.