P. Gamba, F. Dell-'acqua, G. Lisini, and G. Trianni, Improved VHR Urban Area Mapping Exploiting Object Boundaries, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.8, pp.2676-2682, 2007.
DOI : 10.1109/TGRS.2007.899811

A. Farag, R. Mohamed, and A. El-baz, A unified framework for MAP estimation in remote sensing image segmentation, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.7, pp.1617-1634, 2005.
DOI : 10.1109/TGRS.2005.849059

H. Deng and D. Clausi, Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.528-538, 2005.
DOI : 10.1109/TGRS.2004.839589

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 1984.

Z. Kato and J. Zerubia, Markov Random Fields in Image Segmentation. Collection Foundation and Trends in Signal Processing, Now Editor, World Scientific, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737058

J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, pp.259-302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

Z. Kato, J. Zerubia, and M. Berthod, Satellite image classification using a modified Metropolis dynamics, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.573-576, 1992.
DOI : 10.1109/ICASSP.1992.226148

V. Kolmogorov and R. Zabin, What Energy Functions Can Be Minimized via Graph Cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004.
DOI : 10.1007/3-540-47977-5_5

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

C. Benedek, T. Sziranyi, Z. Kato, and J. Zerubia, Detection of Object Motion Regions in Aerial Image Pairs With a Multilayer Markovian Model, IEEE Transactions on Image Processing, vol.18, issue.10, pp.2303-2315, 2009.
DOI : 10.1109/TIP.2009.2025808

Z. Kato and T. Pong, A Multi-Layer MRF Model for Video Object Segmentation, Proceedings of Asian Conference on Computer Vision, pp.953-962, 2006.
DOI : 10.1007/11612704_95

C. Benedek and T. Sziranyi, A Mixed Markov model for change detection in aerial photos with large time differences, 2008 19th International Conference on Pattern Recognition, 2008.
DOI : 10.1109/ICPR.2008.4761658
URL : https://hal.archives-ouvertes.fr/inria-00359116

A. Fridman, Mixed markov models Available: http://www.pnas.org/content Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Contrast stretch and normalization, pp.8092-8096, 2003.

P. Brodtkorb, P. Johannesson, G. Lindgren, I. Rychlik, J. Rydén et al., WAFO -a Matlab toolbox for the analysis of random waves and loads, Proc. 10'th Int. Offshore and Polar Eng. Conf., ISOPE, pp.343-350, 2000.

S. Ghosh, L. Bruzzone, S. Patra, F. Bovolo, and A. Ghosh, A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.3, pp.778-789, 2007.
DOI : 10.1109/TGRS.2006.888861

L. Bruzzone and D. Prieto, An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images, IEEE Transactions on Image Processing, vol.11, issue.4, pp.452-466, 2002.
DOI : 10.1109/TIP.2002.999678