TS-DANet: Truncated SVD Interaction and Dual-Attention Network for Hyperspectral and Multispectral Image Fusion

Fusing hyperspectral image (HSI) and multispectral image (MSI) is essential to merge HSI’s spectral richness with MSI’s spatial detail. This paper introduces TS-DANet, a novel two-part network for HSI-MSI fusion designed to address the limitations of current methods in model prior utilization and spectral-spatial feature extraction. In the initial part, we use truncated singular value decomposition (TSVD) interaction to extract spectral priors from low-resolution HSI (LR-HSI) and spatial sparsity priors from high-resolution MSI (HR-MSI), integrating these through a physics-based optimization for image fusion. We also incorporate a dual-attention mechanism, featuring a dynamic spectral attention module for detailed spectral features and a multi-scale spatial attention module for detailed spatial features. The second part employs a dynamic residual optimization module to further refine spatial and spectral information. Extensive experiments on three remote sensing datasets show that TS-DANet outperforms existing state-of-the-art algorithms in fusion performance. The code will be available at https://github.com.