Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

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 …

The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

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 …

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for
many applications in hyperspectral data exploitation. However, processing such high …

Band selection is one of the most important technique in the reduction of hyperspectral
image (HSI). Different from traditional feature selection problem, an important characteristic …

Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing.
Recently, due to the powerful ability of deep learning (DL) to extract spatial and 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 …

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for
data processing. Band selection (BS), as one of the most commonly used dimension …