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

A. Zare and K. C. Ho, Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing, IEEE Signal Processing Magazine, vol.31, issue.1, pp.95-104, 2014.
DOI : 10.1109/MSP.2013.2279177

B. Somers, M. Zortea, A. Plaza, and G. Asner, Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.396-408, 2012.
DOI : 10.1109/JSTARS.2011.2181340

O. Eches, N. Dobigeon, C. Mailhes, and J. Tourneret, Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1403-1413, 2010.
DOI : 10.1109/TIP.2010.2042993

X. Du, A. Zare, P. Gader, and D. Dranishnikov, Spatial and spectral unmixing using the beta compositional model, IEEE J. Sel. Topics Appl. Earth Observ. in Remote Sens, vol.7, issue.6, pp.1994-2003, 2014.

A. Halimi, N. Dobigeon, and J. Tourneret, Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability, IEEE Transactions on Image Processing, vol.24, issue.12, pp.4904-4917, 2015.
DOI : 10.1109/TIP.2015.2471182

S. Henrot, J. Chanussot, and C. Jutten, Dynamical spectral unmixing of multitemporal hyperspectral images, arXiv preprint, 2015.
DOI : 10.1109/tip.2016.2562562
URL : http://arxiv.org/abs/1510.04238

P. Thouvenin, N. Dobigeon, and J. Tourneret, Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model, IEEE Transactions on Signal Processing, vol.64, issue.2, pp.525-538, 2016.
DOI : 10.1109/TSP.2015.2486746
URL : https://hal.archives-ouvertes.fr/hal-01273078

D. Ralph and H. Xu, Convergence of Stationary Points of Sample Average Two-Stage Stochastic Programs: A Generalized Equation Approach, Mathematics of Operations Research, vol.36, issue.3, pp.568-592, 2011.
DOI : 10.1287/moor.1110.0506

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, J. Mach. Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

J. Bolte, S. Sabach, and M. Teboulle, Proximal alternating linearized minimization for nonconvex and nonsmooth problems, Mathematical Programming, pp.459-494, 2013.
DOI : 10.1007/s10107-013-0701-9
URL : https://hal.archives-ouvertes.fr/hal-00916090

A. Halimi, N. Dobigeon, J. Tourneret, S. Mclaughlin, and P. Honeine, Unmixing hyperspectral images accounting for temporal and spatial endmember variability, Proc. European Signal Process. Conf. (EUSIPCO), pp.1686-1690, 2015.
DOI : 10.1109/eusipco.2015.7362665
URL : https://zenodo.org/record/38913/files/1570099457.pdf

M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne et al., ICE: a statistical approach to identifying endmembers in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.10, pp.2085-2095, 2004.
DOI : 10.1109/TGRS.2004.835299

M. Arngren, M. N. Schmidt, and J. Larsen, Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior, Journal of Signal Processing Systems, vol.3753, issue.2009, pp.479-496, 2011.
DOI : 10.1007/s11265-010-0533-2

]. L. Condat, Fast projection onto the simplex and the ?1 ball, Math. Program, pp.1-11, 2015.
DOI : 10.1007/s10107-015-0946-6
URL : https://hal.archives-ouvertes.fr/hal-01056171

J. Duchi, S. Shalev-schwartz, Y. Singer, and T. Chandra, Efficient projection onto the ?1-ball for learning in high dimensions, Proc. Int. Conf. Machine Learning (ICML), 2008.

J. P. Boyle and R. L. Dykstra, A Method for Finding Projections onto the Intersection of Convex Sets in Hilbert Spaces, Advances in Order Restricted Statistical Inference, pp.28-47, 1986.
DOI : 10.1007/978-1-4613-9940-7_3

R. Heylen, A. Atker, and P. Scheunders, On Using Projection Onto Convex Sets for Solving the Hyperspectral Unmixing Problem, IEEE Geoscience and Remote Sensing Letters, vol.10, issue.6, pp.1522-1526, 2013.
DOI : 10.1109/LGRS.2013.2261276

P. Thouvenin, N. Dobigeon, and J. Tourneret, Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability, IEEE Transactions on Image Processing, vol.25, issue.9, 2015.
DOI : 10.1109/TIP.2016.2579309
URL : https://hal.archives-ouvertes.fr/hal-01500507

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.9473

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

J. M. Bioucas-dias and M. A. Figueiredo, Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010.
DOI : 10.1109/WHISPERS.2010.5594963
URL : http://arxiv.org/abs/1002.4527

J. M. Bioucas-dias, A variable splitting augmented Lagrangian approach to linear spectral unmixing, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009.
DOI : 10.1109/WHISPERS.2009.5289072
URL : http://arxiv.org/abs/0904.4635