Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

The rapid development of deep learning provides a better solution for the end-to-end
reconstruction of hyperspectral image (HSI). However, existing learning-based methods …

Neural networks have dominated the research of hyperspectral image classification,
attributing to the feature learning capacity of convolution operations. However, the fixed …

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 …

Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral
imagery with a high spatial resolution (which we refer to here as H 2 imagery). As a result of …

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

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

Convolutional neural networks (CNNs) have been widely used for hyperspectral image
classification. As a common process, small cubes are first cropped from the hyperspectral …