Abstract Convolutional Neural Networks (CNNs) have become increasingly popular for
hyperspectral image (HSI) classification due to their ability to capture spatial and spectral …

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

Abstract Semi-supervised Graph Neural Networks (GNNs), as an effective data
representation learning framework, have been applied to hyperspectral image (HSI) …

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 …

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

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have led
to promising advancements in hyperspectral image (HSI) classification; however, traditional …

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

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 …