SSDT: Multiscale Spatial–Spectral Dilated Transformer for Hyperspectral and Multispectral Image Fusion

Hyperspectral images (HSIs) and multispectral images (MSIs) possess complementary advantages, with HSI providing rich spectral information and MSI offering fine spatial details. Therefore, fusing HSI and MSI to obtain high-resolution hyperspectral images (HR-HSIs) with both high spatial detail and rich spectral information has drawn increased attention. However, convolutional neural network (CNN)-based methods are limited by their local receptive fields, while traditional Transformer architectures suffer from extremely high computational complexity when applied to HSI with numerous spectral bands. To address these issues, we propose a novel multiscale Spatial-Spectral Dilated Transformer (SSDT) network based on the Transformer framework. The proposed method adopts a dual-branch architecture, consisting of the Spatial Dilated MultiScale Transformer (Spa-DMST) and the Spectral Dilated Positional Embedded Transformer (Spe-DPET) modules. Specifically, Spa-MSDT introduces a multi-level dilated window mechanism to enable cross-scale spatial feature extraction, while Spe-PEDT employs position encoding-enhanced dilated attention to reduce computational complexity. To further optimize the feature fusion process, we design a Gating Fusion Reconstruction (GFR) module that integrates depthwise separable convolutions with a gating mechanism, effectively suppressing redundant information and enhancing detail reconstruction capabilities. Extensive experiments and visualized results on three simulated datasets and one real dataset demonstrate that the proposed method outperforms other state-of-the-art fusion methods. The code of this paper will be available at https://github.com/FxyPd/SSDT.