In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been
attracting increasing attention because of their ability to represent spectral-spatial features …

Nowadays, graph convolutional networks (GCNs) are getting more attention in hyperspectral
image classification (HSIC), and various algorithms based on GCNs have been proposed …

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are
increasing, and pixel-oriented classification has attracted much attention. Recently, graph …

Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …

Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …

Recently, convolutional neural networks (CNNs) have demonstrated impressive capabilities
in the representation and classification of hyperspectral remote sensing images. Traditional …

Although graph convolutional network (GCN) has achieved remarkable success in
hyperspectral image (HSI) classification, most existing GCN-based approaches have failed …

Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …

Abstract Graph Convolutional Network (GCN) has emerged as a new technique for
hyperspectral image (HSI) classification. However, in current GCN-based methods, the …

Graph convolutional networks (GCNs) are a promising approach for addressing the
necessity for long-range information in hyperspectral image (HSI) classification …