C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, 1998.

V. A. Krylov, G. Moser, S. B. Serpico, and J. Zerubia, Dictionary-based probability density function estimation for high-resolution SAR data, Computational Imaging VII, p.72460, 2009.
DOI : 10.1117/12.816102

URL : https://hal.archives-ouvertes.fr/inria-00361384

C. J. Oliver, The interpretation and simulation of clutter textures in coherent images, Inverse Problems, vol.2, issue.4, pp.481-518, 1986.
DOI : 10.1088/0266-5611/2/4/012

A. Voisin, G. Moser, V. A. Krylov, S. B. Serpico, and J. Zerubia, Classification of very high resolution SAR images of urban areas by disctionary-based mixture models, copulas and Markov random fields using textural features, SPIE Symposium Remote Sensing, p.78300, 2010.

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

Q. Chen and P. Gong, Automatic variogram parameter extraction for textural classification of the panchromatic IKONOS imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.5, pp.1106-1115, 2004.
DOI : 10.1109/TGRS.2004.825591

K. Kayabol, E. E. Kuruoglu, J. L. Sanz, B. Sankur, E. Salerno et al., Adaptive Langevin Sampler for Separation of <formula formulatype="inline"><tex Notation="TeX">$t$</tex></formula>-Distribution Modelled Astrophysical Maps, IEEE Transactions on Image Processing, vol.19, issue.9, pp.2357-2368, 2010.
DOI : 10.1109/TIP.2010.2048613

J. Kittler, M. Hatef, R. P. Duin, and J. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998.
DOI : 10.1109/34.667881

G. E. Hinton, Products of experts, 9th International Conference on Artificial Neural Networks: ICANN '99, pp.1-6, 1999.
DOI : 10.1049/cp:19991075

B. Krishnapuram, L. Carin, M. A. Figueiredo, and A. J. Hartemink, Sparse multinomial logistic regression: fast algorithms and generalization bounds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.6, pp.957-968, 2005.
DOI : 10.1109/TPAMI.2005.127

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. on Pattern Anal. Machine Intell, vol.6, issue.6, pp.721-741, 1984.

S. Sanjay-gopal and T. J. Hebert, Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm, IEEE Transactions on Image Processing, vol.7, issue.7, pp.1014-1028, 1998.
DOI : 10.1109/83.701161

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

D. A. Van-dyk, Nesting EM algorithms for computational efficiency, Statistica Sinica, vol.10, pp.203-225, 2000.

C. Liu and D. B. Rubin, ML estimation of the t distribution using EM and its extensions, ECM and ECME, Statistica Sinica, vol.5, pp.19-39, 1995.