HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion

• URL: http://arxiv.org/abs/2602.00490v1
• Date: Sat, 31 Jan 2026 03:24:03 GMT
• Title: HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion
• Authors: Chia-Ming Lee, Yu-Hao Ho, Yu-Fan Lin, Jen-Wei Lee, Li-Wei Kang, Chih-Chung Hsu,
• Abstract summary: Hyperspectral image (HSI) fusion aims to reconstruct a high-resolution HSI (HR-HSI) by combining the rich spectral information of a low-resolution HSI with the fine details of a high-resolution multispectral image (HR-MSI)<n>Recent deep learning methods have achieved notable progress, but they still suffer from limited receptive fields, redundant spectral bands, and the quadratic complexity of self-attention.<n>We propose the Hierarchical Spatial-Spectral Dense Correlation Network (HSSDCT) to overcome these challenges.
• Score: 11.994592153994482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral image (HSI) fusion aims to reconstruct a high-resolution HSI (HR-HSI) by combining the rich spectral information of a low-resolution HSI (LR-HSI) with the fine spatial details of a high-resolution multispectral image (HR-MSI). Although recent deep learning methods have achieved notable progress, they still suffer from limited receptive fields, redundant spectral bands, and the quadratic complexity of self-attention, which restrict both efficiency and robustness. To overcome these challenges, we propose the Hierarchical Spatial-Spectral Dense Correlation Network (HSSDCT). The framework introduces two key modules: (i) a Hierarchical Dense-Residue Transformer Block (HDRTB) that progressively enlarges windows and employs dense-residue connections for multi-scale feature aggregation, and (ii) a Spatial-Spectral Correlation Layer (SSCL) that explicitly factorizes spatial and spectral dependencies, reducing self-attention to linear complexity while mitigating spectral redundancy. Extensive experiments on benchmark datasets demonstrate that HSSDCT delivers superior reconstruction quality with significantly lower computational costs, achieving new state-of-the-art performance in HSI fusion. Our code is available at https://github.com/jemmyleee/HSSDCT.

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