Multiscale Spatial–Spectral Interaction Transformer for Pan-Sharpening

Abstract

Pan-sharpening methods based on deep neural network (DNN) have produced state-of-the-art fusion performance. However, DNN-based methods mainly focus on the modeling of the local properties in low spatial resolution multispectral (LR MS) and panchromatic (PAN) images by convolution neural networks. The global dependencies in the images are ignored. To capture the local and global properties of the images concurrently, we propose a multiscale spatial–spectral interaction transformer (MSIT) for pan-sharpening. Specifically, we construct the multiscale sub-networks containing convolution–transformer encoder to extract the local and global features at different scales from LR MS and PAN images, respectively. Then, a spatial–spectral interaction attention module (SIAM) is designed to merge the features at each scale. In SIAM, the interaction attention is used to decouple the spatial and spectral information efficiently for the enhancement of complementarity and the reduction of redundancy in the extracted features. The features from different scales are further integrated into a multiscale reconstruction module (MRM) to generate the desired high spatial resolution multispectral image, in which the spatial and spectral information is fused scale by scale. The experiments on reduced-and full-scale datasets demonstrate that the proposed MSIT can produce better results in terms of visual and numerical analysis when compared with state-of-the-art methods.