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 …

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 Networks (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

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

Graph neural network (GNN) has recently gained increasing attention in the hyperspectral
image (HSI) classification. Compared with convolutional neural network (CNN), GNN can …

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

With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult
Problem in current research. The graph neural network (GNN) has emerged as an approach …

Convolution neural networks (CNNs) and graph representation learning are two common
methods for hyperspectral image (HSI) classification. Recently, graph convolutional neural …

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

In recent years, the applications of graph convolutional networks (GCNs) in hyperspectral
image (HSI) classification have attracted much attention. However, hyperspectral …