Tensorized High-Order Hypergraph Convolutional Network for Hyperspectral Image Classification

In recent years, graph convolutional networks (GCNs) have gained increasing attention in hyperspectral image (HSI) classification due to their good ability to model the pairwise relationships between two pixels. However, it is difficult to effectively model more complex relationships among multiple pixels with simple graphs. To solve this problem, we propose a novel tensorized high-order hypergraph convolutional network (TH<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCN) for HSI classification. Specifically, the hypergraph structure is employed to effectively model complex spatial relationships between pixels in HSIs, and we propose a new tensor-based algebraic representation of hypergraphs as a powerful strategy for describing the high-order interaction structures of the hypergraph. Besides, by extending the adjacency matrix-based GCN to the tensor domain and exploiting the tensor decomposition, the TH<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCN method is designed to efficiently extract high-order discriminative information from the hypergraph at low complexity for improving HSI classification performance. Furthermore, the construction of the adjacency tensor on all the data requires a huge amount of memory, especially for large-scale remote sensing images. To this end, the TH<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCN is trained and tested for HSI data in a minibatch fashion. Experimental results on three HSI datasets prove that the performance of the proposed method outperforms the comparison methods.