Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

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

Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

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

Object Classification in Remote Sensing Imagery holds paramount importance for extracting
meaningful insights from complex aerial scenes. Conventional methods encounter …

Deep neural networks have achieved promising performance for hyperspectral image (HSI)
classification. However, due to the limitation of the available labeled samples, the traditional …

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 …

Recently, the excellent power of spectral-spatial feature representation of convolutional
neural network (CNN) has gained widespread attention for hyperspectral image (HSI) …

The land-covers within an observed remote sensing scene are usually of different scales;
therefore, the ensemble of multi-scale information is a commonly used strategy to achieve …

Hyperspectral imaging has a strong capability respecting distinguishing surface objects due
to the ability of collect hundreds of bands along the electromagnetic spectrum. Hyperspectral …