The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

Linear hyperspectral unmixing (HU) aims at factoring the observation matrix into an
endmember matrix and an abundance matrix. Linear HU via variational minimum volume …

Hyperspectral images provide much more information than conventional imaging
techniques, allowing a precise identification of the materials in the observed scene, but …

Hyperspectral image unmixing has proven to be a useful technique to interpret
hyperspectral data, and is a prolific research topic in the community. Most of the approaches …

Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching,
require knowing how many substances or materials are present in the scene captured by a …

Abstract Linear Mixing Models (LMMs) are the most popular ones used in the linear
hyperspectral unmixing field. However, several of them do not take into account available …

Hyperspectral data with high dimensionality always needs more storage space and
increases the computational consumption, manifold learning based dimensionality reduction …

Many hyperspectral image processing algorithms (eg, detection, classification, endmember
extraction, and so on) are generally designed with the assumption of no spectral or spatial …

Endmember is defined as the spectral signature of pure material present in hyperspectral
imagery. Estimation of the number of endmembers (NOE) present in a scene is an important …

Spectral unmixing is an inverse problem in hyperspectral imaging that aims at recovering
the spectra of the pure constituents of an image (called endmembers), as well as at …