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Hyperspectral unmixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new linear mixing model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers.doi:10.1109/eusipco.2015.7362496 dblp:conf/eusipco/ThouveninDT15 fatcat:acgbviwklbffpbzv2plb3ghsii