HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion

TL;DR

This paper introduces HSSDCT, a novel deep learning framework for hyperspectral image fusion that explicitly models spatial and spectral correlations, achieving state-of-the-art results with improved efficiency and robustness.

Contribution

The paper proposes a Hierarchical Dense-Residue Transformer and a Spatial-Spectral Correlation Layer to enhance HSI fusion by reducing complexity and spectral redundancy.

Findings

Achieves superior reconstruction quality on benchmark datasets.

Reduces computational costs significantly compared to existing methods.

Outperforms state-of-the-art in HSI fusion accuracy.

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.