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

Fire is a widespread Earth system process with important carbon and climate feedbacks.
Multispectral remote sensing has enabled map** of global spatiotemporal patterns of fire …

Over the past decades, enormous efforts have been made to improve the performance of
linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to …

Variable illumination and environmental, atmospheric, and temporal conditions cause the
measured spectral signature for a material to vary within hyperspectral imagery. By ignoring …

Spectral unmixing is one of the main research topics in hyperspectral imaging. It can be
formulated as a source separation problem, whose goal is to recover the spectral signatures …

Linear spectral unmixing aims at estimating the number of pure spectral substances, also
called endmembers, their spectral signatures, and their abundance fractions in remotely …

Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference
spectral signatures composing the data-referred to as endmembers-their abundance …

Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in
analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors …

Endmember (EM) spectral variability can greatly impact the performance of standard
hyperspectral image analysis algorithms. Extended parametric models have been …

Spectral unmixing is a major technique for the further development of hyperspectral
analysis. It aims to determine the corresponding proportion (fractional abundance) of the …