CNN-Enhanced Hypergraph Attention Network for Hyperspectral Image Classification

Recently, graph neural networks have attracted great attention and achieved outstanding success in hyperspectral image (HSI) classification. However, most existing methods rely on pairwise relationship, neglecting more complex higher-order interactions, which limits the learning of deeply embedded features. Additionally, their dependence on predefined graph structures restricts dynamic node information aggregation. To solve above problems, this paper proposes a CNN enhanced hypergraph attention network (CEHGAT). In order to reveal the high-order interaction in HSI, a hypergraph attention network branch is developed to learn the dynamic connection of hyperedges through the attention mechanism to reveal more representative node embeddings. Then, the CNN enhanced branch uses two multi-scale convolutional blocks to enhance the spatial-spectral features. Finally, the features captured by two branches are fused to realize the complementary advantages of superpixel-level and pixel-level features. Experiments on three benchmark HSI datasets demonstrate that CEHGAT outperforms other state-of-the-art methods with limited labeled samples.