| Summary: | Graph convolutional neural networks (GCNs) have been widely used in hyperspectral images (HSIs) classification for their superiority in processing non-Euclidean structure data. The performance of GCNs relies on the initial graph structure. Most GCN models only utilize spectral information to construct a graph, which is inaccurate because they fail to take the relationship between adjacent nodes into consideration. In addition, due to the over-smooth phenomenon, most GCN models are shallow and unable to extract effective features. To address these issues, a dual graph u-nets is proposed by integrating spatial graph and spectral graph for HSIs classification, denoted by DGU-HSI. To integration the spectral and spatial information, two graphs are constructed for feature extraction. The spectral graph is created by spectral similarity among all pixels where multirange spectral information is retained, and the spatial graph is constructed by exploiting the neighborhood relationship of the center pixel, which describes spatial information. Then, a dual GCN is utilized to extract spatial and spectral graph features simultaneously. To relieve the over-smooth phenomenon, the graph u-nets structure is applied on constructed spectral and spatial graph to extract effective features. Finally, the extracted spectral and spatial features are fused for classification. Experiments conducted on the public datasets demonstrate the effectiveness of the proposed method on HSIs classification.
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