Hyperspectral image classification using a residual enhanced feature fusion hypergraph neural network

Abstract

HyperGraph Neural Network (HGNN) has recently emerged as a promising approach for hyperspectral image classification (HSIC), reconciling state-of-the-art performance with powerful representation capabilities. However, existing HGNN-based methods have limited ability for hypergraph structure exploitation, leading to imperfect classification results. In this paper, we propose a framework called the residual enhanced hypergraph Neural Network (ResHGNN) to discover the potential structural features in hyperspectral image (HSI) data during deep neural networks. Specifically, ResHGNN first generates hyperedges from spatial-spectral features to construct a hypergraph representing fused spatial-spectral feature relationships in HSI. Then, the higher-order relationship among fused modal features is optimized by a residual enhanced hypergraph convolution learning process, to circumvent the HGNN-related over-smoothing issue. Experiments over three popular hyperspectral datasets show that the proposed classification method yields better performance than other models on the visual and numerical comparison.