Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

Hyperspectral image (HSI) classification is challenging due to spatial variability caused by
complex imaging conditions. Prior methods suffer from limited representation ability, as they …

Hyperspectral images (HSIs) contain abundant information in the spatial and spectral
domains, allowing for a precise characterization of categories of materials. Convolutional …

The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in
recent years has garnered a lot of research space. This study examines and analyses over …

Convolutional neural network (CNN) has exhibited enormous potentials in hyperspectral
image (HSI) classification owing to excellent locally modeling ability. Although excellent …

Most hyperspectral image (HSI) classification methods rely on square patch sampling to
incorporate spatial information, thereby facilitating the label prediction of the center pixel …

Graph convolutional networks (GCNs) have shown great potential for few-shot hyperspectral
image (HSI) classification. Mainstream GCNs construct graphs according to single-scale …

Supported by deep learning techniques, remote sensing scene classification, a fundamental
task in remote image analysis, has recently obtained remarkable progress. However, due to …

Hyperspectral images (HSIs) have been playing an important role in the field of ground
object classification because of their rich spatial and spectral information. Aiming at how to …

Researchers have investigated the potential of transformer-based models in remote sensing
(RS) applications, such as scene categorization, after their recent success in natural …