A multi-scale spatial-conditioned spectral routing network for hyperspectral and multispectral image fusion

Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct high spatial resolution hyperspectral images by jointly leveraging the complementary spectral information from hyperspectral imagery and the spatial details from high spatial resolution multispectral imagery. Existing deep learning-based methods typically improve fusion performance by enlarging the receptive field to capture richer spatial–spectral contextual information, which inevitably increase computational complexity and may introduce redundant spatial–spectral interactions. To address these issues, this study proposes a new multiscale Spatial-Conditioned Spectral Routing Network (SCSRNet). Specifically, a Dynamic Fusion Gate (DFG) module is first developed to adaptively regulate the contributions of hyperspectral and multispectral features based on local spatial characteristics. Subsequently, a Large-Kernel Spatial–Spectral Residual Block (LK-SSRB) is designed, in which a Multi-Scale Spatial Perception Module (MSPM) captures spatial contextual information with large receptive fields, while a Spatial-Conditioned Spectral Routing Module (SCSRM) is innovatively designed to dynamically establish spectral dependencies guided by spatial priors. Additionally, a Residual Scaling Connection (RSC) is introduced to improve optimization stability and feature propagation efficiency. Extensive experiments conducted on three public datasets (i.e. Pavia Center, Chikusei, and MDAS) and a self-collected UAV-based Daxing dataset have demonstrated that the proposed method consistently outperforms seven state-of-the-art approaches in both quantitative metrics and visual quality. The performance gains of the proposed SCSRNet observed on the MDAS dataset achieves improvements of 6.0%, 9.8%, 0.7%, 9.7%, and 7.2% over the second-best comparative method. Furthermore, SCSRNet achieves superior reconstruction performance with relatively low computational complexity, underscoring its effectiveness and practicality for HMIF.