In recent years, sparse self-representation has achieved remarkable success in
hyperspectral band selection. However, the traditional sparse self-representation-based …

Soil texture is one of the important factors affecting the physical and chemical properties of
soil, and is generally divided into sand, silt, and clay. Understanding its spatial distribution is …

Band selection (BS), which effectively reduces spectral dimensionality, stands out as a
leading focus within hyperspectral image (HSI) analysis. Self-representation (SR) has …

Hyperspectral band selection is viewed as an effective dimension reduction method.
Recently, researchers present graph-based clustering for hyperspectral image (HSI) …

As one of the most important techniques for hyperspectral image dimensionality reduction,
band selection has received considerable attention, whereas self-representation subspace …

Band selection is an important step in the dimensionality reduction processing of
hyperspectral images and is highly important for eliminating redundant spectral information …

Unsupervised band selection identifies informative bands in hyperspectral images (HSIs)
without prior labeling, reducing spectral redundancy. Besides spectral information, the …

As an effective strategy for reducing the noisy and redundant information for hyperspectral
imagery (HSI), hyperspectral band selection intends to select a subset of original …

In recent years, subspace clustering has become increasingly popular and achieved great
success in band selection (BS) of hyperspectral imagery. However, current subspace …

Hyperspectral imaging acquires information in an extensive range of narrow and contiguous
spectral bands. However, information redundancy in neighboring bands directly affects …