Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …

The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …

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 …

As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …

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 …

For dimensionality reduction of HSI, many clustering-based unsupervised band selection
(UBS) methods have been proposed due to their superiority of reducing the high …

Accurately monitoring the quantity and quality of urban vegetation contributes to regional
greening efforts and improves the understanding of vegetation's impact on the environment …

Abstract Background and Objective Medical hyperspectral images (MHSIs) are used for a
contact-free examination of patients without harmful radiation. However, high-dimensionality …