S 2 -Differential Feature Awareness Network for Hyperspectral Image Fusion

The fusion of hyperspectral image (HSI) and multispectral image (MSI) aims to fully exploit the complementary advantages of both modalities to generate high-resolution HSI. However, existing HSI fusion methods fail to fully utilize frequency domain information when capturing deep correlations between spectral bands, leading to difficulties in accurately preserving spatial texture details. Furthermore, these methods often overlook the intrinsic differential characteristics in HSI fusion, which easily causes spatial–spectral decoupling distortions. To address the aforementioned challenges, this paper proposes a Spatial–Spectral (S2-) Differential Feature Awareness Network (SSDAN) that employs a multi-scale input strategy to fully preserve the rich spatial structures in the source images. Specifically, a Spatial Frequency Fusion Network (SFFN) with a dual-branch architecture is designed to separately model multi-level spatial-frequency features of HSI and MSI, thereby effectively enhancing the joint representation of high-frequency details and global-local structures. Additionally, we introduce a Differential Spatial–Spectral Complementarity Module (DSSCM) that explicitly models modality-specific detailed features and adaptively aligns the dual-branch fused features with the shallow features, effectively mitigating decoupling distortions caused by feature mismatches. The code will be available at https://github.com/MSFLabX/SSDAN.