What question did this study set out to answer?

To improve the fusion of hyperspectral and multispectral images while maintaining spectral and spatial consistency.

March 7, 2026Open Access

A Dual-Stream State-Space Fusion Network with Implicit Neural Representation for Hyperspectral–Multispectral Image Fusion

BLBaisen LiuHarbin Normal University SWShuaiwei WangHeilongjiang Institute of Technology HCHongxia ChuHeilongjiang Institute of Technology

Key Points

To improve the fusion of hyperspectral and multispectral images while maintaining spectral and spatial consistency.
Proposed DSIR-Net (DSIR) architecture with dual-stream representation learning.
Implemented implicit neural representation for coordinate-conditioned refinement.
Applied state-space modeling for efficient global context aggregation during fusion.
Tested on four benchmark datasets for evaluation.
Achieved PSNR improvements across datasets: 0.040 dB (Houston), 0.204 dB (PaviaU), 0.093 dB (Botswana), 0.163 dB (Chikusei).
Demonstrated sharper structural details and preserved spectral information more faithfully compared to competing methods.

Abstract

Hyperspectral–multispectral (HSI–MSI) image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSIs) by combining the spectral fidelity of low-resolution HSIs (LR-HSIs) with the spatial details of high-resolution MSIs (HR-MSIs). A key challenge is preserving spectral–spatial consistency under cross-modal resolution mismatch, where inadequate long-range dependency modeling and unstable inter-modality interaction may induce spectral distortion and structural discontinuities. This paper proposes DSIR-Net (DSIR), a dual-stream state-space fusion architecture equipped with an implicit neural representation (INR) module. DSIR decouples spectral and spatial representation learning into two coordinated streams and leverages state-space modeling to aggregate global context efficiently during progressive fusion. Moreover, INR-based coordinate-conditioned refinement provides continuous sub-pixel compensation, enhancing high-frequency detail recovery while suppressing fusion-induced artifacts. Across four commonly used benchmark datasets, DSIR shows consistent advantages over the competing methods in both numerical metrics and visual reconstruction quality. In addition to sharper structural details, DSIR preserves spectral information more faithfully. Using the best result among the baselines on each dataset as reference, the PSNR improvements are 0.040 dB (Houston), 0.204 dB (PaviaU), 0.093 dB (Botswana), and 0.163 dB (Chikusei).

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