Band selection, as a special case of the feature selection problem, tries to remove redundant
bands and select a few important bands to represent the whole image cube. This has …

A particle swarm optimization (PSO)-based system is proposed to select bands and
determine the optimal number of bands to be selected simultaneously, which is near …

A firefly algorithm (FA) inspired band selection and optimized extreme learning machine
(ELM) for hyperspectral image classification is proposed. In this framework, FA is to select a …

A particle swarm optimization (PSO)-based dimensionality reduction approach is proposed
to use a simple searching criterion function, called minimum estimated abundance …

Hyperspectral images (HSI) contain a great number of bands, which enable better
characterization of features. However, the huge dimension and information volume brought …

Dimensionality reduction of hyperspectral images is essential for reduction of computational
complexity and faster analysis. A novel method for band reduction has been proposed here …

Dimensionality reduction (DR) is of great significance for simplifying and optimizing
hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise …

With hundreds of spectral bands, the rise of the issue of dimensionality in the classification of
hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony …

Hyperspectral images (HSIs) contain hundreds of bands, which provide a wealth of spectral
information and enable better characterization of features. However, the excessive …

In recent years, satellite imaging technologies have resulted in an increased number of
bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing …