Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to semi-supervised classification, and the application of GNN to hyperspectral images has attracted much attention. However, in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features, which does not take full advantage of various graph neural networks (graph filters). Moreover, the traditional GNNs have the problem of oversmoothing. To alleviate these shortcomings, we introduce a deep hybrid multi-graph neural network (DHMG), where two different graph filters, i.e., the spectral filter and the autoregressive moving average (ARMA) filter, are utilized in two branches. The former can well extract the spectral features of the nodes, and the latter has a good suppression effect on graph noise. The network realizes information interaction between the two branches and takes good advantage of different graph filters. In addition, to address the problem of oversmoothing, a dense network is proposed, where the local graph features are preserved. The dense structure satisfies the needs of different classification targets presenting different features. Finally, we introduce a GraphSAGE-based network to refine the graph features produced by the deep hybrid network. Extensive experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-of-the-art models.