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 paper proposes a progressive hybrid-order hypergraph framework (PHHF) that establishes a graph-hypergraph collaborative learning paradigm to learn multi-scale hybrid-order features across hierarchical graphs. Specifically, by synergistically modeling multiple structural information, we develop a progressive hypergraph neural network 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 to 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.