A Comparative Study of Recent Multi-Component Unmixing Algorithms

EasyChair Preprint 4753

Abstract

In this paper, we consider the problem of blind multicomponent image unmixing. Two mixing models are considered : the linear mixing model (LMM) and its extended version (ELMM) which take the spectral (i.e. endmember) variability into account. We introduce powerful unmixing algorithms utilizing these models of latest state-of-the-art, and compare their performance on endmember recovery and abundance estimation.

Keyphrases: Evaluation Assessment, hyperspectral image, multispectral image, spectral unmixing

@booklet{EasyChair:4753,
  author    = {Zhang Mo and Stéphane Pezeril},
  title     = {A Comparative Study of Recent Multi-Component Unmixing Algorithms},
  howpublished = {EasyChair Preprint 4753},
  year      = {EasyChair, 2020}}