A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

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 …

Band selection is a well-known approach to reduce the dimensionality of hyperspectral
imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and …

Band selection (BS) plays an important role in the dimensionality reduction of hyperspectral
data. However, as to the existing BS methods, few are specially designed for target …

This paper presents an unsupervised approach to band selection in hyperspectral images
that considers both spectral and spatial information in data dimensionality reduction. The …

This paper proposes to use band selection-based dimensionality reduction (BS-DR)
technique in addressing a challenging multi-temporal hyperspectral images change …

Semisupervised band selection (BS) technique plays an important role in processing
hyperspectral images (HSIs) because of its superiority of using the limited labeled data and …

Band selection is one of the main methods of reducing the number of dimensions in a
hyperspectral image. Recently, various methods have been proposed to address this issue …

Convolutional neural networks (CNN) provide state-of-the-art performance in many
computer vision tasks, including those related to remote-sensing image analysis …

A new density peak clustering (DPC) algorithm with adaptive clustering center based on
differential privacy was proposed to solve the problems of poor adaptability of high …