J. Bioucas-dias, A. Plaza, G. Camps-valls, P. Scheunders, N. Nasrabadi et al., Hyperspectral Remote Sensing Data Analysis and Future Challenges, Geoscience and Remote Sensing Magazine, pp.6-36, 2013.
DOI : 10.1109/MGRS.2013.2244672

M. Veganzones, G. Tochon, M. Dalla-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

G. Camps-valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods, IEEE Signal Processing Magazine, vol.31, issue.1, pp.45-54, 2014.
DOI : 10.1109/MSP.2013.2279179

N. Nasrabadi, Hyperspectral Target Detection : An Overview of Current and Future Challenges, IEEE Signal Processing Magazine, vol.31, issue.1, pp.34-44, 2014.
DOI : 10.1109/MSP.2013.2278992

T. Akgun, Y. Altunbasak, and R. Mersereau, Super-resolution reconstruction of hyperspectral images, IEEE Transactions on Image Processing, vol.14, issue.11, pp.1860-1875, 2005.
DOI : 10.1109/TIP.2005.854479

W. Ma, J. Bioucas-dias, T. Chan, N. Gillis, P. Gader et al., A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing, IEEE Signal Processing Magazine, vol.31, issue.1, pp.67-81, 2014.
DOI : 10.1109/MSP.2013.2279731

J. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, Selected Topics in Applied Earth Observations and Remote Sensing, pp.354-379, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760787

R. Heylen, M. Parente, and P. Gader, A review of nonlinear hyperspectral unmixing methods Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, pp.1844-1868, 2014.

N. Dobigeon, J. Tourneret, C. Richard, J. 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

A. Zare and K. 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

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 Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, pp.1994-2003, 2014.

J. Plaza, E. Hendrix, I. García, G. Martín, and A. Plaza, On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms, Journal of Mathematical Imaging and Vision, vol.42, issue.3, pp.163-175, 2012.
DOI : 10.1007/s10851-011-0276-0

J. Nascimento and J. Dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data Geoscience and Remote Sensing, IEEE Transactions on, vol.43, pp.898-910, 2005.

M. E. Winter, N-findr: an algorithm for fast autonomous spectral endmember determination in hyperspectral data, Proc. SPIE, pp.266-275, 1999.

J. Li and J. 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

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

Y. Altmann, N. Dobigeon, and J. Tourneret, Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm, IEEE Transactions on Image Processing, vol.23, issue.6, pp.2663-2675, 2014.
DOI : 10.1109/TIP.2014.2314022
URL : https://hal.archives-ouvertes.fr/hal-00992112

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

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

B. Somers, M. Zortea, A. Plaza, and G. Asner, Automated extraction of image-based endmember bundles for improved spectral unmixing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.5, pp.396-408, 2012.

M. Iordache, J. Bioucas-dias, and A. Plaza, Sparse unmixing of hyperspectral data Geoscience and Remote Sensing, IEEE Transactions on, vol.49, pp.2014-2039, 2011.

J. Jin, B. Wang, and L. Zhang, A novel approach based on fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery Geoscience and Remote Sensing Letters, IEEE, vol.7, pp.699-703, 2010.

D. 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. Nascimento and J. Dias, Does independent component analysis play a role in unmixing hyperspectral data?, Geoscience and Remote Sensing, pp.175-187, 2005.

X. C. Garcia, Evaluation des performances de l'analyse statistique et physique d'images hyperspectrales de Mars, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00648102

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

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

M. Kowalski, K. Siedenburg, and M. Dorfler, Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators, IEEE Transactions on Signal Processing, vol.61, issue.10, pp.2498-2511, 2013.
DOI : 10.1109/TSP.2013.2250967
URL : https://hal.archives-ouvertes.fr/hal-00691774

M. Iordache, J. Bioucas-dias, and A. Plaza, Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.11, pp.4484-4502, 2012.
DOI : 10.1109/TGRS.2012.2191590

C. Bilen, G. Puy, R. Gribonval, and L. Daudet, Convex Optimization Approaches for Blind Sensor Calibration Using Sparsity, IEEE Transactions on Signal Processing, vol.62, issue.18, pp.4847-4856, 2014.
DOI : 10.1109/TSP.2014.2342651
URL : https://hal.archives-ouvertes.fr/hal-00853225

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

A. M. Cord, P. C. Pinet, Y. Daydou, and S. D. , Experimental determination of the surface photometric contribution in the spectral reflectance deconvolution processes for a simulated martian crater-like regolithic target, Icarus, vol.175, issue.1, pp.78-91, 2005.
DOI : 10.1016/j.icarus.2004.08.010

R. Marrero, S. Douté, A. Plaza, and J. Chanussot, Validation of spectral unmixing methods using photometry and topography information, Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.

J. Bioucas-dias and J. Nascimento, Hyperspectral subspace identification Geoscience and Remote Sensing, IEEE Transactions on, vol.46, pp.2435-2445, 2008.

C. Jutten, Doctorès Sciences degrees in signal processing from Grenoble Institute of Technology (GIT), France, in 1981 and 1987, respectively. From 1982, he was an Associate Professor at GIT, before being Full Professor at University Joseph Fourier of Grenoble For 35 years, his research interests have been machine learning and source separation , including theory (separability, source separation in nonlinear mixtures, sparsity, multimodality) and applications (brain and hyperspectral imaging, chemical sensor array, speech) He is author or coauthor of more than 90 papers in international journals, 4 books, 25 keynote plenary talks and about 200 communications in international conferences. He has been visiting professor at Swiss Federal Polytechnic Institute, at Riken labs (Japan, 1996) and at Campinas University, 1989.