期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 14, 期 -, 页码 4561-4572出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3074469
关键词
Hyperspectral imaging; Feature extraction; Deep learning; Image classification; Training; Image reconstruction; Classification algorithms; Deep contextual; graph convolutional network (GCN); hyperspectral image classification; multiscale graph
类别
资金
- National Natural Science Foundation of China [41404022]
- National Natural Science Foundation of Shanxi Province [2015JM4128]
In this article, a multiscale graph sample and aggregate network with a context-aware learning method is proposed for HSI classification. This network can learn global and contextual information of the graph effectively, and solve the impact of original input graph errors on classification. Experimental results show the superiority of the proposed method over state-of-the-art methods.
Recently, graph convolutional network (GCN) has achieved promising results in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate the new node. Besides, the existing GCN-based methods divide graph construction and graph classification into two stages ignoring the influence of constructed graph error on classification results. Moreover, the available GCN-based methods fail to understand the global and contextual information of the graph. In this article, we propose a novel multiscale graph sample and aggregate network with a context-aware learning method for HSI classification. The proposed network adopts a multiscale graph sample and aggregate network (graphSAGE) to learn the multiscale features from the local regions graph, which improves the diversity of network input information and effectively solves the impact of original input graph errors on classification. By employing a context-aware mechanism to characterize the importance among spatially neighboring regions, deep contextual and global information of the graph can be learned automatically by focusing on important spatial targets. Meanwhile, the graph structure is reconstructed automatically based on the classified objects as network training, which is able to effectively reduce the influence of the initial graph error on the classification result. Extensive experiments are conducted on three real HSI datasets, which are demonstrated to outperform the compared state-of-the-art methods.
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