W. Ma, J. M. Bioucas-dias, J. Chanussot, and P. Gader, Signal and Image Processing in Hyperspectral Remote Sensing [From the Guest Editors], IEEE Signal Processing Magazine, vol.31, issue.1, pp.22-23, 2014.
DOI : 10.1109/MSP.2013.2282417

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

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

L. Drumetz, J. Chanussot, and C. Jutten, Endmember variability in spectral unmixing: recent advances, Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp.1-4, 2016.

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-01165114

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

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. Goenaga, M. C. Torres-madronero, M. Velez-reyes, S. J. Van-bloem, and J. D. Chinea, Unmixing Analysis of a Time Series of Hyperion Images Over the Gu??nica Dry Forest in Puerto Rico, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.2, pp.329-338, 2013.
DOI : 10.1109/JSTARS.2012.2225096

K. Canham, . Schlamm, B. Ziemann, D. Basener, and . Messinger, Spatially Adaptive Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.11, pp.4248-4262, 2011.
DOI : 10.1109/TGRS.2011.2169680

M. A. Veganzones, G. Tochon, M. D. Mura, A. Plaza, and J. Chanussot, Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation, IEEE Transactions on Image Processing, vol.23, issue.8, pp.3574-3589, 2014.
DOI : 10.1109/TIP.2014.2329767

URL : https://hal.archives-ouvertes.fr/hal-01010430

S. Valero, P. Salembier, and J. Chanussot, Hyperspectral Image Representation and Processing With Binary Partition Trees, IEEE Transactions on Image Processing, vol.22, issue.4, pp.1430-1443, 2013.
DOI : 10.1109/TIP.2012.2231687

URL : https://hal.archives-ouvertes.fr/hal-00798351

A. Robin, K. Cawse-nicholson, A. Mahmood, and M. Sears, Estimation of the Intrinsic Dimension of Hyperspectral Images: Comparison of Current Methods, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2854-2861, 2015.
DOI : 10.1109/JSTARS.2015.2432460

L. Drumetz, M. A. Veganzones, R. Marrero-gómez, G. Tochon, M. D. Mura et al., Hyperspectral Local Intrinsic Dimensionality, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.7, pp.4063-4078, 2016.
DOI : 10.1109/TGRS.2016.2536480

URL : https://hal.archives-ouvertes.fr/hal-01292198

M. Iordache, J. M. Bioucas-dias, and A. Plaza, Collaborative Sparse Regression for Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.1, pp.341-354, 2014.
DOI : 10.1109/TGRS.2013.2240001

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.3258

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

K. Cawse-nicholson, S. B. Damelin, M. Robin, and . Sears, Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory, IEEE Transactions on Image Processing, vol.22, issue.4, pp.1301-1310, 2013.
DOI : 10.1109/TIP.2012.2227765

M. Grana and M. A. Veganzones, An endmember-based distance for content based hyperspectral image retrieval, Pattern Recognition, vol.45, issue.9, pp.3472-3489, 2012.
DOI : 10.1016/j.patcog.2012.03.015

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

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.3832&rep=rep1&type=pdf

R. Ammanouil, A. Ferrari, C. Richard, and D. Mary, Blind and Fully Constrained Unmixing of Hyperspectral Images, IEEE Transactions on Image Processing, vol.23, issue.12, pp.5510-5518, 2014.
DOI : 10.1109/TIP.2014.2362056

URL : http://arxiv.org/pdf/1403.0289

M. Kowalski, Sparse regression using mixed norms, Applied and Computational Harmonic Analysis, vol.27, issue.3, pp.303-324, 2009.
DOI : 10.1016/j.acha.2009.05.006

URL : https://hal.archives-ouvertes.fr/hal-00202904

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, The Annals of statistics, pp.407-499, 2004.

Y. Hu, E. Chi, and G. I. Allen, ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning, 2015.
DOI : 10.1111/j.1467-9868.2005.00532.x

URL : http://arxiv.org/abs/1504.06637

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

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

M. B. Priestley, The Spectral Analysis of Time Series., Journal of the Royal Statistical Society. Series A (Statistics in Society), vol.151, issue.3, 1981.
DOI : 10.2307/2983035