Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

The spectral signatures of the materials contained in hyperspectral images, also called
endmembers
(
EMs
), can be significantly affected by variations in atmospheric, illumination, and environmental conditions that typically occur within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the EMs, which propagates significant modeling errors throughout the whole unmixing process and compromises the quality of the results. Therefore, serious efforts have been dedicated to mitigating the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to enable the EMs to vary within a hyperspectral image, using, for instance, sets of spectral signatures known a priori as well as Bayesian, parametric, and local EM models.