R. Mller, M. Bachmann, C. Makasy, A. D. Miguel, A. Mller et al., Enmap -the future hyperspectral satellite mission product generation, ISPRS Hannover Workshop HighResolution Earth Imaging for Geospatial Information, pp.1-4, 2009.

M. Drusch, U. D. Bello, S. Carlier, O. Colin, V. Fernandez et al., Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote Sensing of Environment, vol.120, pp.25-36, 2012.
DOI : 10.1016/j.rse.2011.11.026

W. Wagner, A Better Understanding of Our Earth through Remote Sensing, Remote Sensing, vol.1, issue.1, 2009.
DOI : 10.3390/rs1010001

D. Donoho, High-dimensional data analysis: the curses and blessing of dimensionality, AMS Mathematical challenges of the 21st century, 2000.

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.
DOI : 10.1109/TIT.1968.1054102

D. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, 2003.
DOI : 10.1002/0471723800

C. J. Burges, Dimension Reduction: A Guided Tour, Machine Learning, pp.275-365, 2010.
DOI : 10.1561/2200000002

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.1006

J. Ham, Y. Chen, M. Crawford, and J. Ghosh, Investigation of the random forest framework for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.492-501, 2005.
DOI : 10.1109/TGRS.2004.842481

F. Ratle, G. Camps-valls, and J. Weston, Semisupervised Neural Networks for Efficient Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.5, pp.2271-2282, 2010.
DOI : 10.1109/TGRS.2009.2037898

G. Camps-valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis, 2009.
DOI : 10.1002/9780470748992

E. Christophe, J. Michel, and J. Inglada, Remote Sensing Processing: From Multicore to GPU, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.4, issue.3, pp.643-652, 2011.
DOI : 10.1109/JSTARS.2010.2102340

A. Plaza, Q. Du, Y. Chang, and R. L. King, High Performance Computing for Hyperspectral Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.4, issue.3, pp.528-544, 2011.
DOI : 10.1109/JSTARS.2010.2095495

E. Christophe, J. Inglada, and A. Giros, Orfeo toolbox: a complete solution for mapping from high resolution satellite images, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.1263-1268, 2008.

C. Bouveyron and C. Brunet-saumard, Model-based clustering of high-dimensional data: A review, Computational Statistics & Data Analysis, vol.71, pp.52-78, 2014.
DOI : 10.1016/j.csda.2012.12.008

URL : https://hal.archives-ouvertes.fr/hal-00750909

L. O. Jimenez and D. A. Landgrebe, Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.1, pp.39-54, 1998.
DOI : 10.1109/5326.661089

F. E. Fassnacht, C. Neumann, M. Förster, H. Buddenbaum, A. Ghosh et al., Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.2547-2561, 2014.
DOI : 10.1109/JSTARS.2014.2329390

I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing ), 2006.
DOI : 10.1007/978-3-540-35488-8

A. Villa, J. A. Benediktsson, J. Chanussot, and C. Jutten, Hyperspectral Image Classification With Independent Component Discriminant Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.12, pp.4865-4876, 2011.
DOI : 10.1109/TGRS.2011.2153861

URL : https://hal.archives-ouvertes.fr/hal-00607195

L. Bruzzone, F. Roli, and S. B. Serpico, An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection, IEEE Transactions on Geoscience and Remote Sensing, vol.33, issue.6, pp.1318-1321, 1995.
DOI : 10.1109/36.477187

]. B. Demir and S. Ertürk, Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images, International Journal of Remote Sensing, vol.5093, issue.6, pp.1801-1807, 2008.
DOI : 10.1109/TCSVT.2006.875210

A. W. Whitney, A Direct Method of Nonparametric Measurement Selection, IEEE Transactions on Computers, vol.20, issue.9, pp.1100-1103, 1971.
DOI : 10.1109/T-C.1971.223410

T. Marill and D. Green, On the effectiveness of receptors in recognition systems, IEEE Transactions on Information Theory, vol.9, issue.1, pp.11-17, 1963.
DOI : 10.1109/TIT.1963.1057810

P. Somol, P. Pudil, J. Novovi?ová, and P. Pacl?k, Adaptive floating search methods in feature selection, Pattern Recognition Letters, vol.20, issue.11-13, pp.1157-1163, 1999.
DOI : 10.1016/S0167-8655(99)00083-5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5032

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, issue.1/3, pp.389-422, 2002.
DOI : 10.1023/A:1012487302797

J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping, Use of the zero-norm with linear models and kernel methods, Journal of machine learning research, vol.3, pp.1439-1461, 2003.

D. Tuia, R. Flamary, and N. Courty, Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.272-285, 2015.
DOI : 10.1016/j.isprsjprs.2015.01.006

URL : https://hal.archives-ouvertes.fr/hal-01103078

M. Fauvel, C. Dechesne, A. Zullo, and F. Ferraty, Fast forward feature selection of hyperspectral images for classification with gaussian mixture models Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.8, issue.6, pp.2824-2831, 2015.

C. Fraley and A. E. Raftery, Model-Based Clustering, Discriminant Analysis, and Density Estimation, Journal of the American Statistical Association, vol.97, issue.458, pp.611-631, 2000.
DOI : 10.1198/016214502760047131

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.5734

D. A. Reynolds and R. C. Rose, Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE transactions on speech and audio processing, pp.72-83, 1995.
DOI : 10.1109/89.365379

R. J. Hathaway, A constrained formulation of maximum-likelihood estimation for normal mixture distributions The Annals of Statistics, pp.795-800, 1985.

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995.
DOI : 10.1016/0031-3203(94)00125-6

URL : https://hal.archives-ouvertes.fr/inria-00074643

A. E. Hoerl and R. W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.24, issue.1, pp.55-67, 1970.
DOI : 10.2307/1909769

A. C. Jensen, A. Berge, and A. S. Solberg, Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.10, pp.2814-2822, 2008.
DOI : 10.1109/TGRS.2008.2001169

R. G. Congalton and K. Green, Assessing the accuracy of remotely sensed data: principles PCAd practices, 2008.

D. M. Powers, Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation, Journal of Machine Learning Technologies, vol.2, 2011.

T. J. Hastie, R. J. Tibshirani, and J. H. Friedman, The elements of statistical learning : data mining, inference, and prediction, ser. Springer series in statistics, 2009.

S. Amari, H. Nagaoka, and D. Harada, Methods of information geometry, ser. Translations of mathematical monographs, Providence, R.I. American Mathematical Society Oxford, 2000.

S. Kullback, Letter to the editor: The kullback-leibler distance, The American Statistician, 1987.

L. Bruzzone and C. Persello, A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability Geoscience and Remote Sensing, IEEE Transactions on, vol.47, issue.9, pp.3180-3191, 2009.

K. B. Petersen and M. S. Pedersen, The matrix cookbook, 2012.

A. R. Webb, Statistical pattern recognition Orfeo toolbox, 2003.
DOI : 10.1002/9781119952954

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

M. D. Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, Morphological Attribute Profiles for the Analysis of Very High Resolution Images, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.10, pp.3747-3762, 2010.
DOI : 10.1109/TGRS.2010.2048116

J. Inglada, M. Arias, B. Tardy, O. Hagolle, S. Valero et al., Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery, Remote Sensing, vol.7, issue.9, pp.123562072-4292, 2015.
DOI : 10.3390/rs70912356

H. B. Mann and D. R. Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, The annals of mathematical statistics, pp.50-60, 1947.
DOI : 10.1214/aoms/1177730491

S. B. Serpico and G. Moser, Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.2, pp.484-495, 2007.
DOI : 10.1109/TGRS.2006.886177

S. D. Backer, P. Kempeneers, W. Debruyn, and P. Scheunders, A band selection technique for spectral classification, IEEE Geoscience and Remote Sensing Letters, vol.2, issue.3, pp.319-323, 2005.

T. M. France and . Sc, degree in Machine Learning from the Paris Saclay University, both in 2016 He is currently a Ph.D. student at the National Polytechnic Institute of Toulouse, within the Signal and Communications Group of the IRIT Laboratory. He is working on the subject of multi-resolution learning for hierarchical analysis of hyperspectral and hypertemporal images under the supervision of Nicolas Dobigeon and Mathieu Fauvel His research interests are remote sensing