J. M. Bioucas-dias and A. Plaza, An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches, Geoscience and Remote Sensing Symposium (IGARSS), pp.1135-1138, 2011.

J. M. 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

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

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

M. P. José, J. M. Nascimento, and . Bioucas-dias, Nonlinear mixture model for hyperspectral unmixing, pp.74770-74770, 2009.

A. Halimi, Y. Altmann, N. Dobigeon, and J. Tourneret, Nonlinear unmixing of hyperspectral images using a generalized bilinear model, Geoscience and Remote Sensing, pp.4153-4162, 2011.

I. Meganem, P. Deliot, X. Briottet, Y. Deville, and S. Hosseini, Linear–Quadratic Mixing Model for Reflectances in Urban Environments, Geoscience and Remote Sensing, pp.544-558, 2014.
DOI : 10.1109/TGRS.2013.2242475

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

G. Camps-valls and L. Bruzzone, Kernel-based methods for hyperspectral image classification Geoscience and Remote Sensing, IEEE Transactions on, vol.43, issue.6, pp.1351-1362, 2005.

J. Bieniarz, E. Aguilera, X. X. Zhu, R. Muller, and P. Reinartz, Joint sparsity model for multilook hyperspectral image unmixing Geoscience and Remote Sensing Letters, IEEE, vol.12, issue.4, pp.696-700, 2015.

J. Bieniarz, R. Muller, X. Zhu, and P. Reinartz, On the use of overcomplete dictionaries for spectral unmixing, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), pp.1-4, 2012.
DOI : 10.1109/WHISPERS.2012.6874232

J. Sigurdsson, O. Magnus, . Ulfarsson, R. Johannes, and . Sveinsson, Endmember constrained semi-supervised hyperspectral unmixing, Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), p.11, 2014.

L. Kantorovich, On the Translocation of Masses, Journal of Mathematical Sciences, vol.133, issue.4, pp.199-201, 1942.
DOI : 10.1007/s10958-006-0049-2

Y. Rubner, C. Tomasi, and L. J. Guibas, A metric for distributions with applications to image databases, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp.59-66, 1998.
DOI : 10.1109/ICCV.1998.710701

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, Advances in Neural Information Processing Systems, pp.2292-2300, 2013.

J. Benamou, G. Carlier, M. Cuturi, L. Nenna, and G. Peyré, Iterative Bregman Projections for Regularized Transportation Problems, SIAM Journal on Scientific Computing, vol.37, issue.2, pp.1111-1138, 2015.
DOI : 10.1137/141000439
URL : https://hal.archives-ouvertes.fr/hal-01096124

E. Ammannito, . Mc-de-sanctis, . Palomba, . Longobardo, H. Mittlefehldt et al., Olivine in an unexpected location on Vesta???s surface, Nature, vol.103, issue.7478, pp.122-125, 2013.
DOI : 10.1038/nature12665

J. Combe, B. Thomas, . Mccord, A. Lucy, S. Mcfadden et al., Composition of the northern regions of Vesta analyzed by the Dawn mission, Icarus, vol.259, pp.53-71, 2015.
DOI : 10.1016/j.icarus.2015.04.026