Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

In this paper, we propose a new method for hyperspectral images (HSI) classification,
aiming to take advantage of both manifold learning‐based feature extraction and neural …

Band selection, by choosing a set of representative bands in a hyperspectral image, is an
effective method to reduce the redundant information without compromising the original …

Hyperspectral images (HSIs) are a powerful source of reliable data in various remote
sensing applications. But due to the large number of bands, HSI has information …

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 …

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

A fast and robust principal component analysis on Laplacian graph (FRPCALG) method is
proposed to select bands of hyperspectral imagery (HSI). The FRPCALG assumes that a …

Several versions of convolutional neural network (CNN) were developed to classify
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …

Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …