Graph Relation Aggregated Spectral Perception Network for Hyperspectral Classification

doi:10.1109/TGRS.2026.3677864

Graph Relation Aggregated Spectral Perception Network for Hyperspectral Classification

2026-03-26

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DOI：10.1109/TGRS.2026.3677864

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Cross-domain few-shot learning hyperspectral image classification (CDFSL HSIC) presents significant challenges, primarily due to spectral variability across domains and the scarcity of labeled target samples. To tackle these issues, we propose a graph relation aggregated spectral perception network (GRASP-Net), which progressively models spectral and structural information from the sample level to the category level. First, an enhanced spectral perception (ESP) module is developed to refine sample-level features by enhancing discriminative spectral cues while suppressing redundancy. On this basis, a prototype-guided graph construction (PGC) module establishes a sample–prototype bipartite graph, integrating geometric neighborhood relations with nongeometric priors to generate compact and robust category-aware embeddings. Furthermore, a gradient-aware metric function interprets gradients as directional vectors to adaptively refine sample–prototype relations, thereby overcoming the limitations of conventional scalar distance metrics. Extensive experiments on multiple cross-domain benchmarks demonstrate that GRASP-Net achieves superior accuracy and robustness compared with state-of-the-art methods, particularly in scenarios with limited labeled samples and large domain shifts.

Keywords:

Cross-domain few-shot learning (CDFSL)

hyperspectral image classification (HSIC)

prototype network

Graph Relation Aggregated Spectral Perception Network for Hyperspectral Classification

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