Remote sensing (RS) images enable high-resolution information collection from complex
ground objects and are increasingly utilized in the earth observation research. Recently, RS …

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …

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 (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …

Band selection refers to the process of choosing the most relevant bands in a hyperspectral
image. By selecting a limited number of optimal bands, we aim at speeding up model …

Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose
bands based on band correlation and information has become more significant, but also …

Band selection is an effective method to deal with the difficulties in image transmission,
storage, and processing caused by redundant and noisy bands in hyperspectral images …

This article proposes a dual global–local attention network (DGLAnet), which is an end-to-
end unsupervised band selection (UBS) method that fully utilizes spatial and spectral …