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

Defence Technology（防务技术） 2023年第5期23卷 164-176页

作者： Ding Yao Zhang Zhi-li Zhao Xiao-feng Cai Wei He Fang Cai Yao-ming Wei-Wei Cai Xi'an Research Institute of High Technology Xi'an710025China The School of Computer Science China University of GeosciencesWuhan430074China The School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China

With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current *** graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much ***,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 *** 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 *** former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph *** network realizes information interaction between the two branches and takes good advantage of different graph *** addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are *** dense structure satisfies the needs of different classification targets presenting different ***,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid *** experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.

关键词： Graph neural network Hyperspectral image classification deep hybrid network

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