K. Abend, T. J. Harley, and L. N. Kanal, Classification of Binary Random Patterns, IEEE Transactions on Information Theory, vol.11, issue.4, pp.538-544, 1965.

L. Alparone, B. Aiazzi, S. Baronti, and A. Garzelli, Remote Sensing Image Fusion, 2008.

A. Arnab, S. Zheng, S. Jayasumana, B. Romera-paredes, M. Larsson et al., Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction, IEEE Signal Processing Magazine, vol.35, issue.1, pp.37-52, 2018.

P. M. Baggenstoss, A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces, IEEE Transactions on Speech and Audio Processing, vol.9, issue.4, pp.411-416, 2001.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

P. A. Devijver, Hidden Markov mesh random field models in image analysis, J. Applied Stat, vol.20, pp.187-227, 1993.

O. Dikshit and D. P. Roy, An empirical investigation of image resampling effects upon the spectral and textural supervised classification of a high spatial resolution multispectral image, Photogrammetric Engineering and Remote Sensing, vol.62, issue.9, pp.1085-1092, 1996.

R. Fjortoft, Y. Delignon, W. Pieczynski, M. Sigelle, and F. Tupin, Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields, IEEE Trans. Geosci. Rem. Sens, vol.41, pp.675-686, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01347239

J. H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Stat, vol.29, issue.5, pp.1189-1232, 2001.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

I. Hedhli, G. Moser, S. B. Serpico, and J. Zerubia, Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields, IEEE Geosci. Rem. Sens. Lett, vol.14, issue.12, pp.2448-2452, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01632907

I. Hedhli, G. Moser, J. Zerubia, and S. B. Serpico, A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data, IEEE Trans. Geosci. Rem. Sens, vol.54, issue.11, pp.6333-6348, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01308039

J. Inglada, V. Muron, D. Pichard, and T. Feuvrier, Analysis of artifacts in subpixel remote sensing image registration, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.1, pp.254-264, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00578144

V. Kolmogorov, Convergent Tree-Reweighted Message Passing for Energy Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1568-1583, 2006.

J. .. Laferté, P. Pérez, and F. Heitz, Discrete Markov image modeling and inference on the quadtree, IEEE Trans. Image Process, vol.9, issue.3, pp.390-404, 2000.

S. Li, Markov Random Field Modeling in Image Analysis, 2009.

S. Mallat, A wavelet tour of signal processing -The sparse way, 2009.

A. Montaldo, L. Fronda, I. Hedhli, G. Moser, J. Zerubia et al., Causal markov mesh hierarchical modeling for the contextual classification of multiresolution satellite images, Proc. of IEEE ICIP, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02157081

A. Montaldo, L. Fronda, I. Hedhli, G. Moser, J. Zerubia et al., Joint classification of multiresolution and multisensor data using a multiscale markov mesh model, Proc. of IEEE IGARSS, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02157082

G. Moser, A. De-giorgi, and S. B. Serpico, Multiresolution Supervised Classification of Panchromatic and Multispectral Images by Markov Random Fields and Graph Cuts, IEEE Trans. Geosci. Rem. Sens, vol.54, issue.9, pp.5054-5070, 2016.

R. Schapire, The strength of weak learnability, Machine Learning, 1990.

X. Wang, Deep learning in object recognition, detection, and segmentation, Foundations and Trends in Signal Processing, vol.8, issue.4, pp.217-382, 2016.

A. S. Willsky, Multiresolution Markov models for signal and image processing, Proceedings of the IEEE, vol.90, issue.8, pp.1396-1458, 2002.

S. Yousefi and N. Kehtarnavaz, Generating symmetric causal Markov random fields, Electronics letters, vol.47, issue.22, pp.1224-1225, 2011.