Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …

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 …

Analysis of hyperspectral imagery is a critical aspect of remote sensing in precision
agriculture, for which effective dimensionality reduction (DR) strategies for the inherent …

Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard
problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved …

Band selection (BS) is an effective approach to alleviate the spectral redundancy of a
hyperspectral image (HSI). The emerging deep-learning-based BS methods have become a …

Homogeneous band-or pixel-based feature selection, which exploits the difference between
spectral or spatial regions to select informative and low-redundant bands, has been …

Band selection (BS) is an effective means to solve the problems of spectral redundancy and
Hughes phenomenon in hyperspectral images (HSIs). However, existing BS methods fail to …

Abstract Background and Objective Neonatal health is critical for early infant care, where
accurate and timely diagnoses are essential for effective intervention. Traditional methods …

Hyperspectral image (HSI) classification benefits from effectively handling both spectral and
spatial features. However, deep learning (DL) models, like graph convolutional networks …

Band selection (BS) is an efficacious approach to reduce hyperspectral information
redundancy while preserving the physical meaning of hyperspectral images (HSIs) …