M. D. Craig, Minimum-volume transforms for remotely sensed data, IEEE Transactions on Geoscience and Remote Sensing, vol.32, issue.3, pp.542-552, 1994.
DOI : 10.1109/36.297973

D. C. Heinz and C. Chang, Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.3, pp.529-545, 2001.
DOI : 10.1109/36.911111

O. Eches, N. Dobigeon, and J. Tourneret, Estimating the Number of Endmembers in Hyperspectral Images Using the Normal Compositional Model and a Hierarchical Bayesian Algorithm, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.3, pp.582-591, 2010.
DOI : 10.1109/JSTSP.2009.2038212

L. Miao and H. Qi, Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.3, p.765777, 2007.
DOI : 10.1109/TGRS.2006.888466

J. M. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.354-379, 2012.
DOI : 10.1109/JSTARS.2012.2194696
URL : https://hal.archives-ouvertes.fr/hal-00760787

N. Keshava and J. F. Mustard, Spectral unmixing, IEEE Signal Processing Magazine, vol.19, issue.1, pp.44-57, 2002.
DOI : 10.1109/79.974727

J. Chen, C. Richard, and P. Honeine, Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model, IEEE Transactions on Signal Processing, vol.61, issue.2, pp.480-492, 2013.
DOI : 10.1109/TSP.2012.2222390

Y. Altmann, A. Halimi, N. Dobigeon, and J. Tourneret, Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery, IEEE Transactions on Image Processing, vol.21, issue.6, pp.3017-3025, 2012.
DOI : 10.1109/TIP.2012.2187668

B. W. Hapke, Bidirectional reflectance spectroscopy: 1. Theory, Journal of Geophysical Research: Solid Earth, vol.16, issue.B4, pp.3039-3054, 1981.
DOI : 10.1029/JB086iB04p03039
URL : http://hdl.handle.net/2060/19870014000

B. Somers, K. Cools, S. Delalieux, J. Stuckens, D. Van-der-zande et al., Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards, Remote Sensing of Environment, vol.113, issue.6, pp.1183-1193, 2009.
DOI : 10.1016/j.rse.2009.02.003

J. M. Nascimento and J. M. Bioucas-dias, Nonlinear mixture model for hyperspectral unmixing, Image and Signal Processing for Remote Sensing XV, pp.74770-74770, 2009.
DOI : 10.1117/12.830492

W. Fan, B. Hu, J. Miller, and M. Li, Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated???forest hyperspectral data, International Journal of Remote Sensing, vol.30, issue.11, pp.2951-2962, 2009.
DOI : 10.1029/91JE03117

A. Halimi, Y. Altmann, N. Dobigeon, and J. Tourneret, Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.11, pp.4153-4162, 2011.
DOI : 10.1109/TGRS.2010.2098414

I. Meganem, P. Deliot, X. Briottet, Y. Deville, and S. Hosseini, Physical modelling and non-linear unmixing method for urban hyperspectral images, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp.1-4, 2011.
DOI : 10.1109/WHISPERS.2011.6080863

]. P. Gader, D. Dranishnikov, A. Zare, and J. Chanussot, A sparsity promoting bilinear unmixing model, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012.
DOI : 10.1109/WHISPERS.2012.6874255

A. Papoulis, Probability, random variables, and stochastic processes, Communications and signal processing, 1991.

S. M. Kay, Fundamentals of Statistical Signal Processing: Detection theory, 1998.

J. M. Nascimento and J. M. Bioucas-dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005.
DOI : 10.1109/TGRS.2005.844293