Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review
Ricardo Augusto Borsoi, Tales Imbiriba, Jose Carlos Moreira Bermudez, Cedric Richard, Jocelyn Chanussot, Lucas Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten
Abstract
The spectral signatures of the materials contained in hyperspectral images, also called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endmembers</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMs</i> ), 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.