MDC-FusFormer: Multiscale Deep Cross-Fusion Transformer Network for Hyperspectral and Multispectral Image Fusion

The spatial resolution of hyperspectral images (HSIs) is usually limited due to internal imaging mechanisms. To obtain imagery with high spectral and high spatial resolutions, which is essential for subsequent HSI processing tasks, a cost-effective approach is to fuse HSI with multispectral images (MSIs). One highly effective fusion method is the convolutional neural network (CNN). However, CNNs have limitations in capturing global information and complex features. Recently, visual transformers (ViTs) have garnered interest for their ability to process non-local information. Despite this, existing HSI-MSI fusion methods suffer from insufficient spatial-spectral feature interaction, resulting in suboptimal fusion quality. To address these challenges, we propose a multiscale deep cross-fusion transformer (MDC-FusFormer) network for HSI and MSI fusion. This network effectively performs the interactive fusion of spatial-spectral features, thereby enhancing the quality of the fused images. MDC-FusFormer employs a three-branch network architecture consisting of two independent progressive feature mining modules (PFMMs), a multiscale deep cross-fusion attention module, and a spatial-spectral feature fusion module. Initially, shallow features at different scales of MSI and HSI are recursively extracted through successive up- and down-sampling using CNNs. These features then interact with the deep cross-modal information at corresponding scales through the attention block. Finally, a multidimensional refinement convolution block (MRCB) is applied to refine the feature information, which is then combined with cascaded up-sampling to reconstruct the high-resolution fused image step by step. Experimental results on five datasets indicate that, compared to nine other methods, MDC-FusFormer delivers superior performance.