EHGNN: Enhanced Hypergraph Neural Network for Hyperspectral Image Classification
Qingwang Wang, Jiangbo Huang, Tao Shen, Yanfeng Gu
Abstract
Recently, the hypergraph neural network (HGNN) has drawn increasing attention in modeling complex high-order correlations. Compared to simple graph neural networks, HGNNs exhibit more powerful representational ability. There are two limitations in the application of hypergraph theory to hyperspectral image (HSI) classification. One is the inadequate explicit representation of semantic information contained in HSI. Another is the loss of pixel-level spectral-spatial information. Thus, an enhanced hypergraph neural network (EHGNN) is proposed to promote the application of hypergraph theory to HSI classification. Specifically, two important enhancements are introduced: 1) the concept of key hypergraph, providing more rich semantic information and improving the interpretability for complex distribution structures, and 2) the integration of convolutional neural network (CNN) and HGNN architectures into an end-to-end framework, the loss of spectral-spatial information at the pixel-level is effectively reduced. Through these two enhancements, EHGNN exhibits a 4% improvement in overall accuracy (OA) on the Pavia University dataset and a 2% improvement in OA on the Xuzhou dataset compared to HGNN. Furthermore, the test results on two HSI datasets demonstrate that our EHGNN achieves competitive performance compared to other state-of-the-art methods.