Deep learning has certainly become the dominant trend in hyperspectral (HS) remote
sensing (RS) image classification owing to its excellent capabilities to extract highly …

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

Currently, the convolutional neural networks (CNNs) have become the mainstream methods
for hyperspectral image (HSI) classification, due to their powerful ability to extract local …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI)
classification tasks because of their excellent local spatial feature extraction capabilities …

In recent years, convolutional neural networks have continuously dominated the
downstream tasks on hyperspectral remote sensing images with its strong local feature …

Recently, due to the powerful capability at modeling the long-range relationships,
Transformer-based methods have been widely explored in many research areas, including …

Transformer is a powerful tool for capturing long-range dependencies and has shown
impressive performance in hyperspectral image (HSI) classification. However, such power …

Deep learning methods have been widely used in hyperspectral image classification and
have achieved state-of-the-art performance. Nonetheless, the existing deep learning …

Hyperspectral image (HSI) classification is a critical task with numerous applications in the
field of remote sensing. Although convolutional neural networks have achieved remarkable …