J. M. Bioucas-dias, A. Plaza, G. Camps-valls, P. Scheunders, N. M. Nasrabadi et al., Hyperspectral Remote Sensing Data Analysis and Future Challenges, IEEE Geoscience and Remote Sensing Magazine, vol.1, issue.2, pp.6-36, 2013.
DOI : 10.1109/MGRS.2013.2244672
URL : http://www.lx.it.pt/~bioucas/files/ieee_grsm_2013_hyper_rs_data_analysis.pdf

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. 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

R. Heylen, M. Parente, and P. Gader, A Review of Nonlinear Hyperspectral Unmixing Methods, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.1844-1868, 2014.
DOI : 10.1109/JSTARS.2014.2320576

N. Dobigeon, J. Y. Tourneret, C. Richard, J. C. Bermudez, S. Mclaughlin et al., Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms, IEEE Signal Processing Magazine, vol.31, issue.1, pp.82-94, 2014.
DOI : 10.1109/MSP.2013.2279274
URL : https://hal.archives-ouvertes.fr/hal-00915663

B. Somers, G. P. Asner, L. Tits, and P. Coppin, Endmember variability in Spectral Mixture Analysis: A review, Remote Sensing of Environment, vol.115, issue.7, pp.1603-1616, 2011.
DOI : 10.1016/j.rse.2011.03.003

L. Drumetz, G. Tochon, M. A. Veganzones, J. Chanussot, and C. Jutten, Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6190-6194, 2017.
DOI : 10.1109/ICASSP.2017.7953346
URL : https://hal.archives-ouvertes.fr/hal-01581521

S. Henrot, J. Chanussot, and C. Jutten, Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images, IEEE Transactions on Image Processing, vol.25, issue.7, pp.3219-3232, 2016.
DOI : 10.1109/TIP.2016.2562562
URL : https://hal.archives-ouvertes.fr/hal-01346918

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. P. 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
URL : http://www.umbc.edu/rssipl/people/aplaza/Papers/Journals/2012.JSTARS.Bundles.pdf

A. Halimi, N. Dobigeon, and J. Y. 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
URL : http://arxiv.org/pdf/1406.5071

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

M. A. Veganzones, L. Drumetz, R. Marrero, G. Tochon, M. D. Mura et al., A new extended linear mixing model to address spectral variability, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.
DOI : 10.1109/WHISPERS.2014.8077595
URL : https://hal.archives-ouvertes.fr/hal-01010424

L. Drumetz, M. A. Veganzones, S. Henrot, R. Phlypo, J. Chanussot et al., Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability, IEEE Transactions on Image Processing, vol.25, issue.8, pp.3890-3905, 2016.
DOI : 10.1109/TIP.2016.2579259
URL : https://hal.archives-ouvertes.fr/hal-01336279

B. Hapke, Theory of reflectance and emittance spectroscopy, 2012.

L. Drumetz, Endmember variability in hyperspectral image unmixing, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01394809

L. Drumetz, S. Henrot, M. A. Veganzones, J. Chanussot, and C. Jutten, Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.2015-2016, 2015.
DOI : 10.1109/whispers.2015.8075417
URL : https://hal.archives-ouvertes.fr/hal-01336279

J. M. Nascimento and J. M. 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://www.lx.it.pt/~bioucas/files/ieeegrsVca04.pdf

A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra, Clustering on the unit hypersphere using von Mises-Fisher distributions, Journal of Machine Learning Research, vol.6, pp.1345-1382, 2005.

J. Li and J. M. Bioucas-dias, Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, pp.250-253, 2008.
DOI : 10.1109/IGARSS.2008.4779330
URL : http://www.lx.it.pt/~bioucas/files/igarss08.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
URL : http://www.ecel.ufl.edu/~barnes/downloads/berman04ice.pdf

P. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds, 2009.
DOI : 10.1515/9781400830244

L. Condat, Fast projection onto the simplex and the L1 ball, Mathematical Programming, pp.1-11, 2014.
DOI : 10.1007/s10107-015-0946-6
URL : https://hal.archives-ouvertes.fr/hal-01056171

N. Boumal, B. Mishra, P. Absil, and R. Sepulchre, Manopt, a Matlab toolbox for optimization on manifolds, Journal of Machine Learning Research, vol.15, pp.1455-1459, 2014.

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

T. Matsuki, N. Yokoya, and A. Iwasaki, Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest With the Aid of Lidar Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.5, pp.2177-2187, 2015.
DOI : 10.1109/JSTARS.2015.2417859