J. Bibring, OMEGA: Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité, Mars Express: The Scientific Payload, pp.37-49, 2004.

G. Chang, The New Age of Hyperspectral Oceanography, Oceanography, vol.17, issue.2, pp.16-23, 2004.
DOI : 10.5670/oceanog.2004.43

E. Cloutis, B. Vila, A. Bell, and M. Lamothe, Hyperspectral and Luminescence Observer (HALO) Mars mission concept - innovative data triage, compression, processing and analysis for the hyperspectral imager, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.1-4, 2009.
DOI : 10.1109/WHISPERS.2009.5289049

F. Hilton, Hyperspectral Earth Observation from IASI: Five Years of Accomplishments, Bulletin of the American Meteorological Society, vol.93, issue.3, pp.347-370, 2012.
DOI : 10.1175/BAMS-D-11-00027.1

D. S. Kimes, Y. Knyazikhin, J. L. Privette, A. A. Abuelgasim, and F. Gao, Inversion methods for physically???based models, Remote Sensing Reviews, vol.1, issue.2-4, pp.381-439, 2000.
DOI : 10.1109/99.735892

D. Goodenough, B. Victoria, J. Li, G. Asner, M. Schaepman et al., Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems, 2006 IEEE International Symposium on Geoscience and Remote Sensing, pp.2000-2004, 2006.
DOI : 10.1109/IGARSS.2006.518

P. Stenberg, M. Mottus, and M. Rautiainen, Modeling the Spectral Signature of Forests: Application of Remote Sensing Models to Coniferous Canopies, Advances in Land Remote Sensing, pp.147-171, 2008.
DOI : 10.1007/978-1-4020-6450-0_6

M. Shokr, A physics-based remote sensing data fusion approach, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), pp.1050-1052, 2003.
DOI : 10.1109/IGARSS.2003.1294008

L. Foster, B. Brock, M. Cutler, and F. Diotri, A physically based method for estimating supraglacial debris thickness from thermal band remote-sensing data, Journal of Glaciology, vol.58, issue.210, pp.677-691, 2012.
DOI : 10.3189/2012JoG11J194

C. Bernard-michel, S. Douté, M. Fauvel, L. Gardes, and S. Girard, Retrieval of Mars surface physical properties from OMEGA hyperspectral images using regularized sliced inverse regression, Journal of Geophysical Research, vol.20, issue.2, 2009.
DOI : 10.1029/2008JE003171
URL : https://hal.archives-ouvertes.fr/inria-00276116

S. Douté, E. Deforas, F. Schmidt, R. Oliva, and B. Schmitt, A comprehensive numerical package for the modeling of Mars hyperspectral images, Lunar and Planetary Science XXXVIII, 2007.

M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, Advances in Spectral-Spatial Classification of Hyperspectral Images, Proceedings of the IEEE, vol.101, issue.3, pp.652-675, 2013.
DOI : 10.1109/JPROC.2012.2197589
URL : https://hal.archives-ouvertes.fr/hal-00737075

Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, Deep Learning-Based Classification of Hyperspectral Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.2094-2107, 2014.
DOI : 10.1109/JSTARS.2014.2329330

J. Bhatt, M. Joshi, and M. , A Data-Driven Stochastic Approach for Unmixing Hyperspectral Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.1936-1946, 2014.
DOI : 10.1109/JSTARS.2014.2328597

R. Heylen, M. Parente, and P. Gader, A review of nonlinear hyperspectral unmixing methods Sliced inverse regression for dimension reduction, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Journal of the American Statistical Association, vol.7, issue.86 414, pp.1844-1868, 1991.

H. Wu, Kernel Sliced Inverse Regression with Applications to Classification, Journal of Computational and Graphical Statistics, vol.17, issue.3, pp.590-610, 2008.
DOI : 10.1198/106186008X345161

K. P. Adragni and R. D. Cook, Sufficient dimension reduction and prediction in regression, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.68, issue.3, pp.4385-4405, 1906.
DOI : 10.1093/biomet/asm044

L. Xu, M. I. Jordan, and G. E. Hinton, An alternative model for mixtures of experts, Advances in Neural Information Processing Systems, pp.633-640, 1995.

Y. Qiao and N. Minematsu, Mixture of Probabilistic Linear Regressions: A unified view of GMM-based mapping techiques, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3913-3916, 2009.
DOI : 10.1109/ICASSP.2009.4960483

S. Ingrassia, S. C. Minotti, and G. Vittadini, Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions, Journal of Classification, vol.71, issue.3, pp.363-401, 2012.
DOI : 10.1007/s00357-012-9114-3

M. Tipping, Sparse Bayesian learning and the relevance vector machine, The Journal of Machine Learning Research, vol.1, pp.211-244, 2001.

N. Lawrence, Probabilistic non-linear principal component analysis with gaussian process latent variable models, The Journal of Machine Learning Research, vol.6, pp.1783-1816, 2005.

A. Thayananthan, R. Navaratnam, B. Stenger, P. Torr, and R. Cipolla, Multivariate Relevance Vector Machines for Tracking, European Conference on Computer Vision, pp.124-138, 2006.
DOI : 10.1007/11744078_10

A. Deleforge, F. Forbes, and R. Horaud, High-dimensional regression with gaussian mixtures and partially-latent response variables, Statistics and Computing, vol.19, issue.11, 2014.
DOI : 10.1007/s11222-014-9461-5
URL : https://hal.archives-ouvertes.fr/hal-01107604

Y. Tarabalka, M. Fauvel, J. Chanussot, and J. Benediktsson, SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.4, pp.736-740, 2010.
DOI : 10.1109/LGRS.2010.2047711
URL : https://hal.archives-ouvertes.fr/hal-00578864

J. Li, J. M. Bioucas-dias, and A. Plaza, Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.3, pp.809-823, 2012.
DOI : 10.1109/TGRS.2011.2162649

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.1-4, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760787

Y. Altmann, N. Dobigeon, S. Mclaughlin, and J. Tourneret, Residual Component Analysis of Hyperspectral Images???Application to Joint Nonlinear Unmixing and Nonlinearity Detection, IEEE Transactions on Image Processing, vol.23, issue.5, pp.2148-2158, 2014.
DOI : 10.1109/TIP.2014.2312616
URL : https://hal.archives-ouvertes.fr/hal-00984400

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4
URL : https://hal.archives-ouvertes.fr/inria-00072526

L. Chaari, T. Vincent, F. Forbes, M. Dojat, and P. Ciuciu, Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach, IEEE Transactions on Medical Imaging, vol.32, issue.5, pp.821-837, 2013.
DOI : 10.1109/TMI.2012.2225636
URL : https://hal.archives-ouvertes.fr/inserm-00753873

J. Leenaars, Africa soil profiles database, version 1.1. a compilation of geo-referenced and standardized legacy soil profile data for Sub-Saharian Africa (with dataset), Africa Soil Information ServiceAfSIS) project, 2013.
DOI : 10.1201/b16500-13

L. Tits, B. Somers, J. Stuckens, and P. Coppin, Validating nonlinear mixing models: benchmark datasets from vegetated areas, 6th Workshop on Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

S. Douté, Nature and composition of the icy terrains of the south pole of Mars from MEX OMEGA observations, Lunar and Planetary Science XXXVI, 2005.

Q. Sixian, M. Jianwen, and W. Xuanji, Construction and Experiment of Hierarchical Bayesian Network in Data Assimilation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.2, pp.1036-1047, 2013.
DOI : 10.1109/JSTARS.2012.2217316

Y. Dong, J. Wang, C. Li, G. Yang, Q. Wang et al., Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.1, pp.188-201, 2013.
DOI : 10.1109/JSTARS.2012.2208943

A. Salberg, Land Cover Classification of Cloud-Contaminated Multitemporal High-Resolution Images, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.1, pp.377-387, 2011.
DOI : 10.1109/TGRS.2010.2052464