Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have
generated good progress. Meanwhile, graph convolutional networks (GCNs) have also …

Recently, there has been growing interest in hyperspectral images (HSIs) classification
tasks, with both graph neural networks (GNN) and convolutional neural networks (CNNs) …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …

Due to powerful feature extraction capability, convolutional neural networks (CNNs) have
been widely used for hyperspectral image (HSI) classification. However, because of a large …

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

Hyperspectral image (HSI) classification is one of the basic tasks of remote sensing image
processing, which is to predict the label of each HSI pixel. Convolutional neural network …

Classification methods that are based on hyperspectral images (HSIs) are playing an
increasingly significant role in the processes of target detection, environmental …

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

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 …