Nonlinear unmixing of hyperspectral images: Models and algorithms

Nicolas Dobigeon (1, 2) , Jean-Yves Tourneret (3, 2) , Cédric Richard (4) , José Carlos M. Bermudez (5) , Steve Mclaughlin (6) , Alfred O. Hero (7)

1 IRIT-SC - Signal et Communications
2 Toulouse INP - Institut National Polytechnique (Toulouse)
3 IRIT-MINDS - CoMputational imagINg anD viSion
4 LAGRANGE - Joseph Louis LAGRANGE
5 UFSC - Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis]
6 HWU - Heriot-Watt University [Edinburgh]
7 University of Michigan [Ann Arbor]

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling.

Mots clés: Hyperspectral imagery; Spectral unmixing; Nonlinear mixtures

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José Carlos M. Bermudez, Steve Mclaughlin, et al.. Nonlinear unmixing of hyperspectral images: Models and algorithms. IEEE Signal Processing Magazine, 2014, vol. 31, pp. 82-94. ⟨10.1109/MSP.2013.2279274⟩. ⟨hal-00915663⟩