HGTFN: A Hypergraph and Transformer Fusion Network for Hyperspectral Image Classification

Graph Neural Networks (GNNs) have demonstrated significant potential in hyperspectral image (HSI) classification. However, their performance is often constrained by fixed graph structures and limited capacity for long-range dependency modeling. To address these limitations, we propose a novel Hypergraph and Transformer Fusion Network (HGTFN) that integrates hypergraph convolution and Transformer encoding in a complementary manner, combining localized structural modeling with global semantic context aggregation. Specifically, a Structure-aware Multi-hop Hypergraph Construction (SMHGC) module is developed to generate adaptive, multi-scale hypergraphs guided by spatial–spectral correlations, enabling the capture of higher-order relational structures. This is followed by a Hypergraph Convolution with Edge-wise Attention (HGCEA) module, which enhances feature propagation through edge-adaptive weighting. To overcome the rigidity of grid-based tokenization in conventional Vision Transformers (ViTs), we introduce a Key-Pixel Guided Transformer (KPFormer), which constructs localized neighborhood tokens centered on key pixels based on feature similarity and employs a context-aware feedforward block to strengthen local–global interactions. Furthermore, a Residual Complement Fusion Network (RCFN) is incorporated to fuse multi-source features and refine semantic representation in non-key regions. This design enables HGTFN to generate more balanced and discriminative representations even with limited training samples. Experiments conducted on four benchmark datasets, namely Indian Pines, Pavia University, Salinas and WHU-Hi-HongHu, demonstrate that the HGTFN model achieves state-of-the-art performance with overall accuracies (OA) of 95.73%, 95.58%, 99.19% and 96.56%, respectively, significantly outperforming 10 other algorithms, including CNN, Transformer, and graph-based methods. The core code for HGTFN is published at https://github.com/Majunyi310321/HGTFN.