Progressive Hybrid-Order Hypergraph Framework for Hyperspectral Image Classification

Hypergraph neural networks (HGNNs) have garnered considerable attention in hyperspectral image (HSI) classification for their ability to model higher order nonlinear relationships. However, most existing HGNN-based HSI classification methods adopt simple hypergraph structures, which fail to fully leverage low-order and high-order information within HSIs. Moreover, these methods are constrained by rigid single-scale superpixel segmentations, which fail to fully capture rich spectral–spatial features of HSIs. To overcome these limitations, this article proposes a progressive hybrid-order hypergraph framework (PHHF) that establishes a graph-hypergraph collaborative learning paradigm to learn multiscale hybrid-order features across hierarchical graphs. Specifically, by synergistically modeling multiple structural information, we develop a progressive HGNN framework to enhance the extraction of hierarchical representations of HSIs. Second, at each level, a novel hybrid-order hyperedge generation strategy is designed to enhance spectral–spatial consistency across scales and overcome the limitations of existing hypergraph methods. Finally, a novel heterogeneous kernel-based convolution (HetConv) is introduced to enhance pixel-level feature extraction for the PHHF. Compared with conventional convolution, HetConv offers richer spatial–spectral details with lower computational cost. Extensive experiments on widely used HSI datasets demonstrate that our proposed method achieves state-of-the-art HSI classification performance.