V. Alberga, Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications, Remote Sensing, vol.1, issue.3, pp.122-143, 2009.
DOI : 10.3390/rs1030122

F. Baselice, G. Ferraioli, and V. Pascazio, Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.5, pp.995-999, 2014.
DOI : 10.1109/LGRS.2013.2284297

Y. Bazi, L. Bruzzone, and F. Melgani, An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.874-887, 2005.
DOI : 10.1109/TGRS.2004.842441

C. Benedek, X. Descombes, and J. Zerubia, Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.1, pp.33-50, 2012.
DOI : 10.1109/TPAMI.2011.94
URL : https://hal.archives-ouvertes.fr/hal-00730552

C. Benedek and T. Szirányi, Markovian framework for structural change detection with application on detecting built-in changes in airborne images, Int. Conf. on Signal Processing, Pattern Recognition and Applications . ACTA, pp.68-73, 2007.

C. Benedek and T. Szirányi, Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.10, pp.3416-3430, 2009.
DOI : 10.1109/TGRS.2009.2022633

C. Benedek, T. Szirányi, 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

J. Besag, On the statistical analysis of dirty images, Journal of Royal Statistics Society, vol.48, pp.259-302, 1986.

F. Bovolo, L. Bruzzone, and M. Marconcini, A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.7, pp.2070-2082, 2008.
DOI : 10.1109/TGRS.2008.916643

P. Brodtkorb, P. Johannesson, G. Lindgren, I. Rychlik, J. Rydén et al., WAFO -a Matlab toolbox for the analysis of random waves and loads, In: Int. Offshore and Polar Eng. Conf, vol.3, pp.343-350, 2000.

L. Bruzzone and D. Fernandez-prieto, Automatic analysis of the difference image for unsupervised change detection, IEEE Transactions on Geoscience and Remote Sensing, vol.38, issue.3, pp.1171-1182, 2000.
DOI : 10.1109/36.843009

L. Bruzzone and D. Fernandez-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

C. Carincotte, S. Derrode, and S. Bourennane, Unsupervised change detection on SAR images using fuzzy hidden Markov chains, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.2, pp.432-441, 2006.
DOI : 10.1109/TGRS.2005.861007
URL : https://hal.archives-ouvertes.fr/hal-00082617

L. Castellana, A. D-'addabbo, and G. Pasquariello, A composed supervised/unsupervised approach to improve change detection from remote sensing, Pattern Recognition Letters, vol.28, issue.4, pp.405-413, 2007.
DOI : 10.1016/j.patrec.2006.08.010

F. Chatelain, J. Tourneret, and J. Inglada, Change Detection in Multisensor SAR Images Using Bivariate Gamma Distributions, IEEE Transactions on Image Processing, vol.17, issue.3, pp.249-258, 2008.
DOI : 10.1109/TIP.2008.916047
URL : https://hal.archives-ouvertes.fr/hal-00467504

K. Chen, Z. Zhou, H. Lu, and C. Huo, Change detection based on conditional random field models, WSEAS International Conference on Remote Sensing, pp.93-97, 2007.

Y. Chen and Z. Cao, An improved MRF-based change detection approach for multitemporal remote sensing imagery, Signal Processing, vol.93, issue.1, pp.163-175, 2013.
DOI : 10.1016/j.sigpro.2012.07.013

D. Clausi and H. Deng, Design-based texture feature fusion using Gabor filters and co-occurrence probabilities, IEEE Transactions on Image Processing, vol.14, issue.7, pp.925-936, 2005.
DOI : 10.1109/TIP.2005.849319
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.1960

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

D. Fernandez-prieto and M. Marconcini, A Novel Partially Supervised Approach to Targeted Change Detection, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.12, pp.5016-5038, 2011.
DOI : 10.1109/TGRS.2011.2154336

A. Fridman, Mixed Markov models, Proc. National Academy of Sciences of USA, pp.8092-8096, 2003.
DOI : 10.1073/pnas.0731829100

T. Fung and E. Ledrew, The determination of optimal threshold levels for change detection using various accuracy indices, Photogrammetric Engineering and Remote Sensing, vol.54, pp.1449-1454, 1988.

P. Gamba, F. Dell-'acqua, and G. Lisini, Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.10, pp.44-2820, 2006.
DOI : 10.1109/TGRS.2006.879498

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell, vol.6, issue.6, pp.721-741, 1984.

A. Ghosh, B. Subudhi, and L. Bruzzone, Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images, IEEE Transactions on Image Processing, vol.22, issue.8, pp.3087-3096, 2013.
DOI : 10.1109/TIP.2013.2259833

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

T. Hoberg, F. Rottensteiner, R. Feitosa, and C. Heipke, Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.2, pp.659-673, 2015.
DOI : 10.1109/TGRS.2014.2326886

T. Hoberg, F. Rottensteiner, and C. Heipke, Context models for CRF-based classification of multitemporal remote sensing data. ISPRS Annals of Photogrammetry , Remote Sensing and Spatial Information Sciences I-7, pp.129-134, 2012.

V. Hodge and J. Austin, A Survey of Outlier Detection Methodologies, Artificial Intelligence Review, vol.22, issue.2, pp.85-126, 2004.
DOI : 10.1023/B:AIRE.0000045502.10941.a9

J. Inglada and A. Giros, On the possibility of automatic multisensor image registration, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.10, pp.2104-2120, 2004.
DOI : 10.1109/TGRS.2004.835294
URL : https://hal.archives-ouvertes.fr/hal-00569950

P. Jodoin, M. Mignotte, and C. Rosenberger, Segmentation Framework Based on Label Field Fusion, IEEE Transactions on Image Processing, vol.16, issue.10, pp.2535-2550, 2007.
DOI : 10.1109/TIP.2007.903841
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.595

H. Kalayeh and D. Landgrebe, Utilizing Multitemporal Data by a Stochastic Model, IEEE Transactions on Geoscience and Remote Sensing, vol.24, issue.5, pp.792-795, 1986.
DOI : 10.1109/TGRS.1986.289628

Z. Kato and T. C. Pong, A Markov random field image segmentation model for color textured images, Image and Vision Computing, vol.24, issue.10, pp.1103-1114, 2006.
DOI : 10.1016/j.imavis.2006.03.005
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.6503

Z. Kato, T. C. Pong, and G. Q. Song, Multicue MRF image segmentation: combining texture and color features, Object recognition supported by user interaction for service robots, pp.660-663, 2002.
DOI : 10.1109/ICPR.2002.1044836
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.204.77

Z. Kato and J. Zerubia, Markov random fields in image segmentation. Foundations and Trends in Signal Processing, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737058

J. Kim and R. Zabih, Factorial Markov Random Fields, European Conference on Computer Vision, pp.321-334, 2002.
DOI : 10.1007/3-540-47977-5_21

V. Kolmogorov and R. Zabih, 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 : 10.1109/TPAMI.2004.1262177

Y. Kosugi, M. Sakamoto, M. Fukunishi, T. Wei-lu-doihara, and S. Kakumoto, Urban Change Detection Related to Earthquakes Using an Adaptive Nonlinear Mapping of High-Resolution Images, IEEE Geoscience and Remote Sensing Letters, vol.1, issue.3, pp.152-156, 2004.
DOI : 10.1109/LGRS.2004.828917

S. Kumar, M. Anouncia, S. Johnson, A. Agarwal, and P. Dwivedi, Agriculture change detection model using remote sensing images and GIS: Study area Vellore, 2012 International Conference on Radar, Communication and Computing (ICRCC), pp.54-57, 2012.
DOI : 10.1109/ICRCC.2012.6450547

D. Liu, K. Song, J. R. Townshend, and P. Gong, Using local transition probability models in Markov random fields for forest change detection, Remote Sensing of Environment, vol.112, issue.5, pp.2222-2231, 2008.
DOI : 10.1016/j.rse.2007.10.002

W. Liu and V. Prinet, Probabilistic Modeling for Structural Change Inference, Asian Conference on Computer Vision, pp.836-846, 2006.
DOI : 10.1007/11612032_84
URL : https://hal.archives-ouvertes.fr/inria-00124202

A. Lorette, X. Descombes, and J. Zerubia, Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area extraction from satellite images, International Journal of Computer Vision, vol.36, issue.3, pp.221-236, 2000.
DOI : 10.1023/A:1008129103384

S. Martinis, A. Twele, and S. Voigt, Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.1, pp.251-263, 2011.
DOI : 10.1109/TGRS.2010.2052816

F. Melgani and Y. Bazi, Markovian Fusion Approach to Robust Unsupervised Change Detection in Remotely Sensed Imagery, IEEE Geoscience and Remote Sensing Letters, vol.3, issue.4, pp.457-461, 2006.
DOI : 10.1109/LGRS.2006.875773

F. Melgani and S. Serpico, A markov random field approach to spatio-temporal contextual image classification, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.11, pp.2478-2487, 2003.
DOI : 10.1109/TGRS.2003.817269

G. Moser, E. Angiati, and S. Serpico, Multiscale Unsupervised Change Detection on Optical Images by Markov Random Fields and Wavelets, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.4, pp.725-729, 2011.
DOI : 10.1109/LGRS.2010.2102333

S. Patra, S. Ghosh, and A. Ghosh, Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network, 2007 International Conference on Computing: Theory and Applications (ICCTA'07), pp.716-720, 2007.
DOI : 10.1109/ICCTA.2007.128

R. Potts, Some generalized order-disorder transformations, Proceedings of the Cambridge Philosophical Society. No. 48, p.106, 1952.
DOI : 10.1103/PhysRev.60.252

Y. Qi and Z. Rongchun, A CMRF-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images, 2007 8th International Conference on Electronic Measurement and Instruments, pp.898-904, 2007.
DOI : 10.1109/ICEMI.2007.4350825

S. Reed, T. Ruiz, I. Capus, C. Petillot, and Y. , The fusion of large scale classified side-scan sonar image mosaics, IEEE Transactions on Image Processing, vol.15, issue.7, pp.2049-2060, 2006.
DOI : 10.1109/TIP.2006.873448

S. Serpico and G. Moser, Weight Parameter Optimization by the Ho–Kashyap Algorithm in MRF Models for Supervised Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.12, pp.3695-3705, 2006.
DOI : 10.1109/TGRS.2006.881118

M. Shadaydeh and T. Szirányi, An improved local similarity measure estimation for change detection in remote sensing images, 2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, pp.230-234, 2014.
DOI : 10.1109/ICARES.2014.7024381
URL : http://eprints.sztaki.hu/8043/1/Shadaydeh_2726494_234_z.pdf

W. Shuang and H. Leung, A Markov Random Field Approach for Sidescan Sonar Change Detection, IEEE Journal of Oceanic Engineering, vol.37, issue.4, pp.659-669, 2012.
DOI : 10.1109/JOE.2012.2206677

P. Singh, Z. Kato, and J. Zerubia, A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences, 2014 22nd International Conference on Pattern Recognition, 2014.
DOI : 10.1109/ICPR.2014.169
URL : https://hal.archives-ouvertes.fr/hal-01016820

A. Solberg, T. Taxt, and A. Jain, A Markov random field model for classification of multisource satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.34, issue.1, pp.100-113, 1996.
DOI : 10.1109/36.481897

B. N. Subudhi, F. Bovolo, A. Ghosh, and L. Bruzzone, Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images, optical Image Processing, pp.284-292, 2014.
DOI : 10.1016/j.optlastec.2013.10.003

R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields, European Conference on Computer Vision, pp.16-29, 2006.
DOI : 10.1007/3-540-47977-5_58

T. Szirányi and M. Shadaydeh, Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.9, pp.1544-1548, 2014.
DOI : 10.1109/LGRS.2014.2300873

F. Wang, Y. Wu, Q. Zhang, P. Zhang, M. Li et al., Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model, IEEE Geoscience and Remote Sensing Letters, vol.10, issue.4, pp.697-701, 2013.
DOI : 10.1109/LGRS.2012.2219494

R. Wiemker, An iterative spectral-spatial Bayesian labeling approach for unsupervised robust change detection on remotely sensed multispectral imagery, Int. Conf. on Computer Analysis of Images and Patterns, pp.263-270, 1997.
DOI : 10.1007/3-540-63460-6_126
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.2195

Q. Xu, Y. Pu, W. Wang, and H. Zhong, Multispectral remote sensing image change detection based on Markovian fusion, 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics), 2012.
DOI : 10.1109/Agro-Geoinformatics.2012.6311725

R. Xu and I. Wunsch, Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, pp.645-678, 2005.
DOI : 10.1109/TNN.2005.845141

P. Zhong and R. Wang, A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.12, pp.3978-3988, 2007.
DOI : 10.1109/TGRS.2007.907109