In the last decade, there has been a significant surge of interest in machine learning,
primarily driven by advancements in deep learning (DL). DL has emerged as a powerful …

With the development of hyperspectral sensors, more and more hyperspectral images can
be acquired, and the pixel-oriented classification of hyperspectral images has attracted the …

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

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 …

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

Recently, graph neural networks (GNNs) have been demonstrated to be a promising
framework in investigating non-Euclidean dependency in hyperspectral (HS) images. Since …

In hyperspectral image (HSI) classification, objects corresponding to pixels of different
classes exhibit varying size characteristics, which causes a challenge for effective pixelwise …

The segmentation algorithm for buildings and waters is extremely important for the efficient
planning and utilization of land resources. The temporal and space range of remote sensing …

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

Land scene classification in satellite imagery has a wide range of applications in remote
surveillance, environment monitoring, remote scene analysis, Earth observations and urban …