Hypergraph Convolutional Network With Multiple Hyperedges Fusion for Hyperspectral Image Classification Under Limited Samples

Graph convolutional network (GCN) combined with convolutional neural network (CNN) exhibits significant potential in hyperspectral image (HSI) classification. Hypergraph convolutional network (HGCN) can address the limitations of GCN-based methods in representing high-order nonlinear relationships among multiple nodes. However, the existing pixel-based HGCN methods mostly adopt partial pixels for hypergraph modeling, thus limiting the representation of the global structure. Additionally, both pixel-based and superpixel-based HGCN methods only utilize the k-nearest neighbors (kNN) for hyperedge representation, thereby ignoring the rich topological information in HSI segmentation regions. To tackle these issues, we propose an HGCN with multiple hyperedges fusion (HGCN-MHF) for HSI classification with limited samples. First, we introduce a CNN branch for capturing spatial and spectral pixel-level features with different receptive fields, which begins with denoising and spectral transformation, followed by cross multiscale convolution (CMC). Second, we design an HGCN branch for extracting superpixel-level features guided by diversified high-order hypergraph structures, which incorporates a multiple hyperedges fusion (MHF) module followed by hypergraph convolution (HGC). Finally, a score-weighted feature fusion (SWFF) strategy is proposed to balance and promote the feature fusion of the two branches. Experimental results on four benchmark HSI datasets demonstrate that HGCN-MHF outperforms other state-of-the-art methods, with improvements in terms of overall accuracy (OA) around 3.50%–20.47% (Indian Pines), 2.85%–19.83% (University of Pavia), 2.31%–8.75% (Salinas), and 3.13%–20.03% (WHU-Hi-HongHu) under five labeled samples per class.