C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar images, 2004.

R. E. Schapire, A brief introduction to boosting, Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp.1401-1406, 1999.

B. Waske and J. A. Benediktsson, Fusion of Support Vector Machines for Classification of Multisensor Data, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.12, pp.3858-3866, 2007.
DOI : 10.1109/TGRS.2007.898446

A. Ani and M. Deriche, A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence, Journal Of Artificial Intelligence Research, vol.17, pp.333-361, 2011.

J. A. Benediktsson and P. H. Swain, Consensus theoretic classification methods, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.4, pp.688-704, 1992.
DOI : 10.1109/21.156582

C. Graffigne, F. Heitz, P. Perez, F. Preteux, M. Sigelle et al., <title>Hierarchical Markov random field models applied to image analysis: a review</title>, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, pp.2-17, 1995.
DOI : 10.1117/12.216341

V. Krylov, G. Moser, S. B. Serpico, and J. Zerubia, Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.554-566, 2011.
DOI : 10.1109/JSTSP.2010.2103925

URL : https://hal.archives-ouvertes.fr/inria-00562326

R. B. Nelsen, An introduction to copulas, 2006.
DOI : 10.1007/978-1-4757-3076-0

J. Laferte, P. Perez, and F. Heitz, Discrete Markov image modeling and inference on the quadtree, IEEE Transactions on Image Processing, vol.9, issue.3, pp.390-404, 2000.
DOI : 10.1109/83.826777

A. Voisin, V. Krylov, G. Moser, S. B. Serpico, and J. Zerubia, Multichannel hierarchical image classification using multivariate copulas, Computational Imaging X, p.82960, 2012.
DOI : 10.1117/12.917298

URL : https://hal.archives-ouvertes.fr/hal-00667187

S. G. Mallat, A wavelet tour of signal processing, 2008.

I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on Pure and Applied Mathematics, vol.34, issue.7, pp.909-996, 1988.
DOI : 10.1002/cpa.3160410705

M. Berthod, Z. Kato, S. Yu, and J. Zerubia, Bayesian image classification using Markov random fields, Image and Vision Computing, vol.14, issue.4, pp.285-295, 1996.
DOI : 10.1016/0262-8856(95)01072-6

URL : https://hal.archives-ouvertes.fr/hal-01087878

C. Bouman and M. Shapiro, A multiscale random field model for Bayesian image segmentation, IEEE Transactions on Image Processing, vol.3, issue.2, pp.162-177, 1994.
DOI : 10.1109/83.277898

G. Celeux, D. Chauveau, and J. Diebolt, Stochastic versions of the em algorithm: an experimental study in the mixture case, Journal of Statistical Computation and Simulation, vol.67, issue.4, pp.287-314, 1996.
DOI : 10.1214/aos/1176346060

URL : https://hal.archives-ouvertes.fr/hal-00693519

C. Tison, J. Nicolas, F. Tupin, and H. Maitre, A new statistical model for Markovian classification of urban areas in high-resolution SAR images, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.10, pp.2046-2057, 2004.
DOI : 10.1109/TGRS.2004.834630

URL : https://hal.archives-ouvertes.fr/hal-00556167

S. Parrilli, M. Poderico, C. V. Angelino, and L. Verdoliva, A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.2, pp.606-616, 2012.
DOI : 10.1109/TGRS.2011.2161586