R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

F. Kayitakire, C. Hamel, and P. Defourny, Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery, Remote Sensing of Environment, vol.102, issue.3-4, pp.390-401, 2006.
DOI : 10.1016/j.rse.2006.02.022

I. Champion, P. Dubois-fernandez, D. Guyon, and M. Cottrel, Radar image texture as a function of forest stand age, International Journal of Remote Sensing, vol.29, issue.6, pp.1795-1800, 2008.
DOI : 10.1109/36.739113

M. N. Do and M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, IEEE Transactions on Image Processing, vol.11, issue.2, pp.146-158, 2002.
DOI : 10.1109/83.982822
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.2426

O. Regniers, L. Bombrun, D. Guyon, J. Samalens, C. Tinel et al., Wavelet based texture modeling for the classification of very high resolution maritime pine forest images, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.2027-2030, 2014.
DOI : 10.1109/IGARSS.2014.6946861
URL : https://hal.archives-ouvertes.fr/hal-01064405

O. Regniers, L. Bombrun, D. Guyon, J. Samalens, and C. Germain, Wavelet-Based Texture Features for the Classification of Age Classes in a Maritime Pine Forest, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.3, pp.621-625, 2015.
DOI : 10.1109/LGRS.2014.2353656

I. Champion, C. Germain, J. P. Da-costa, A. Alborini, and P. Dubois-fernandez, Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.1, pp.5-9, 2014.
DOI : 10.1109/LGRS.2013.2244060
URL : https://hal.archives-ouvertes.fr/hal-00903994

L. Bombrun, Y. Berthoumieu, N. Lasmar, and G. Verdoolaege, Mutlivariate texture retrieval using the geodesic distance between elliptically distributed random variables, IEEE ICIP, pp.3637-3640, 2011.

G. Verdoolaege and P. Scheunders, On the Geometry of Multivariate Generalized Gaussian Models, Journal of Mathematical Imaging and Vision, vol.16, issue.2, pp.180-193, 2012.
DOI : 10.1007/s10851-011-0297-8

R. Kwitt and A. Uhl, Lightweight Probabilistic Texture Retrieval, IEEE Transactions on Image Processing, vol.19, issue.1, pp.241-253, 2010.
DOI : 10.1109/TIP.2009.2032313

Y. Stitou, N. Lasmar, and Y. Berthoumieu, Copulas based multivariate gamma modeling for texture classification, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1045-1048, 2009.
DOI : 10.1109/ICASSP.2009.4959766
URL : https://hal.archives-ouvertes.fr/hal-00399615

M. Salicru, D. Morales, M. L. Menendez, and L. Pardo, On the Applications of Divergence Type Measures in Testing Statistical Hypotheses, Journal of Multivariate Analysis, vol.51, issue.2, pp.372-391, 1994.
DOI : 10.1006/jmva.1994.1068

A. D. Nascimento, R. J. Cintra, and A. C. Frery, Hypothesis Testing in Speckled Data With Stochastic Distances, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.1, pp.373-385, 2010.
DOI : 10.1109/TGRS.2009.2025498

M. L. Williams, PolSARproSim: A Coherent, Polarimetric SAR Simulation of Forests for PolSARPro. Design Document ans Algorithm Specification, 2006.

J. P. Maugé, Le pin maritime premier résineux de France, 1987.

I. Ilea, L. Bombrun, C. Germain, R. Terebes, and M. Borda, Statistical hypothesis test for robust classification on the space of covariance matrices, 2015 IEEE International Conference on Image Processing (ICIP), 2015.
DOI : 10.1109/ICIP.2015.7350802
URL : https://hal.archives-ouvertes.fr/hal-01228770