Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …

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 (BS) work for hyperspectral remote sensing images (HRSIs) has attracted
increasing attention from researchers. In the HRSI, the salient features of each band always …

Hyperspectral image (HSI) classification is an important task in many applications, such as
environmental monitoring, medical imaging, and land use/land cover (LULC) classification …

Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as
new-generation neural network models with nonlinear learning units, ie, the generative …

Unsupervised hyperspectral image (HSI) band selection methods have been attracting ever-
increasing attention. However, the local structural features captured by most of the existing …

Unsupervised band selection is fundamental to alleviate the curse of dimensionality for
hyperspectral imagery. Although many research works have been developed, it is still a …

Deep learning-based informative band selection methods on hyperspectral images (HSI)
recently have gained intense attention to eliminate spectral correlation and redundancies …

Abstract State Representation Learning (SRL) is a field in Robotics and Artificial Intelligence
that studies how to encode the observations of an environment in a way that facilitates …