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Graph convolutional networks (GCNs) have been shown to be effective for hyperspectral image (HSI) classification due to their capacity to learn representations of spatial–spectral features. However, the existing GCN-based models heavily rely on predefined receptive fields to capture and aggregate neighbor information for each node, which limits the ability to adaptively selecting the most significant receptive field from graph data. To address the aforementioned problem, in this article, we propose a novel dynamic adaptive sampling GCN (DAS-GCN) algorithm that captures neighbor information through adaptive sampling to allow the receptive field to be dynamically obtained. The basic underlying idea is that the most meaningful receptive field for each target node can be adaptively discovered, and the edge adjacency weights can be adjusted simultaneously after each adaptive sampling operation. Thus, we enable the graph to be dynamically updated and refined. Specifically, the adaptive sampling operation consists of two complementary components; in the first step, the importance of different remote nodes in a large-scale neighborhood is learned, while in the second step, rich underlying spatial–spectral information is extracted from local neighbors and filtered. The proposed model has the ability to learn how to extensively exploit spectral–spatial correlations from both local and remote nodes. Moreover, the proposed DAS-GCN model has a superior ability to leverage node feature information to naturally generalize and efficiently generate node embeddings for unseen data. The experimental results with overall accuracy on four real HSI datasets, i.e., Indian Pines, Pavia university, Houston 2013, and Salinas are 95.63%, 96.40%, 94.70%, and 99.08%, respectively, which clearly demonstrate the advantages of the proposed method compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

While semisupervised methods based on graph convolutional networks (GCNs) can achieve good results in hyperspectral image (HSI) classification, their performance is limited as they rely only on spatial–spectral similarity when constructing local adjacency graphs. Moreover, relying on local adjacency graphs can limit the ability of the methods to ensure the consistency of global feature in complex hyperspectral remote sensing environments. Using the spatial–spectral information is typically not sufficient for providing a reliable similarity measurement to construct a global graph when the spectral variability is large and the spatial distance is long in intraclass pixels. To address this issue, this article presents a novel globally consistent GCN (GCGCN) for HSI classification. According to the proposed method, a local reliable initial graph is first built from inherent spatial–spectral information by considering this graph as a variable to optimize. Then, adaptive global high-order neighbors are explored to capture the underlying rich spatial contextual information by utilizing the graph topological consistent connectivity instead of the common strategy of using only the spatial–spectral similarity measurement. Finally, the adaptive global high-order graph structure and two-layer networks are combined to achieve the global feature smoothing of the same class samples and maintain high global feature consistency. The proposed GCGCN method is evaluated on three real HSI data sets to demonstrate its superiority compared to ten different classification methods. Moreover, the proposed GCGCN method achieves state-of-the-art classification results on three data sets in terms of four classification evaluation metrics, including overall accuracy (OA), kappa coefficient (KC), average accuracy (AA), and class accuracy (CA). [ABSTRACT FROM AUTHOR]