The immense representation power of deep learning frameworks has kept them in the
spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks …

The application of graph convolutional networks (GCN) in hyperspectral image (HSI)
classification has become a promising method, thanks to its flexible convolution operation in …

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

Convolutional neural network (CNN) has been broadly adopted on hyperspectral image
(HSI) processing due to its impressive feature extraction capabilities. Nevertheless, it is still a …

Hyperspectral images (HSIs) contain hundreds of continuous spectral bands and are rich in
spectral‐spatial information. In terms of HSIs' classification, traditional convolutional neural …

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

Graph convolutional networks (GCNs) can extract features of samples in non-Euclidean
space, which can be used for hyperspectral image (HSI) classification in collaboration with …

Recently, convolutional neural network (CNN) and graph convolutional network (GCN) have
been used widely for hyperspectral image (HSI) classification which, respectively, specialize …

Graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral
image classification. However, most of the available GCN-based HSI classification methods …

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