. .. La-comparaison, Caractéristiques des bases de données UCI utilisés pour

D. Dubois, Possibility theory and statistical reasoning, Computational Statistics & Data Analysis, vol.51, issue.1, pp.47-69, 2006.
DOI : 10.1016/j.csda.2006.04.015

URL : http://www.irit.fr/~Didier.Dubois/Papers0804/D_CSDA06.pdf

Q. Dong and X. Liu, Risk assessment of water security in haihe river basin during drought periods based on D-S evidence theory, Water Science and Engineering, vol.7, issue.2, pp.119-132, 2014.

I. Hammami, G. Mercier, A. Hamouda, and J. Dezert, Kohonen???s Map Approach for the Belief Mass Modeling, IEEE Transactions on Neural Networks and Learning Systems, vol.27, issue.10, pp.2060-2071, 2016.
DOI : 10.1109/TNNLS.2015.2480772

I. Hammami, G. Mercier, and A. Hamouda, The Kohonen map for credal classification of large multispectral images, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.3706-3709, 2014.
DOI : 10.1109/IGARSS.2014.6947288

I. Hammami and G. Mercier, Kohonen-based credal fusion of heterogeneous data: application to optical and radar joint classification with missing data, IEEE Transactions on Geoscience and Remote Sensing, p.2016
DOI : 10.1109/igarss.2015.7326433

I. Hammami, G. Mercier, and A. Hamouda, The Kohonen map for credal fusion of heterogeneous data, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.2947-2950, 2015.
DOI : 10.1109/IGARSS.2015.7326433

I. Hammami, G. Mercier, and J. Dezert, Copulas-based fusion of consonant belief functions induced by dependent sources of evidences. Knowledge Based Systems, p.2017

P. Smets, The combination of evidence in the transferable belief model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.5, pp.447-458, 1990.
DOI : 10.1109/34.55104

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.6, issue.2, pp.191-234, 1994.
DOI : 10.1007/978-3-540-44792-4_28

URL : https://hal.archives-ouvertes.fr/hal-01185821

A. Martin, About Conflict in the Theory of Belief Functions, Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, 2012.
DOI : 10.1007/978-3-642-29461-7_19

R. Kennes, Computational aspects of the Mobius transformation of graphs, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.2, pp.201-223, 1992.
DOI : 10.1109/21.148425

R. Kennes and P. Smets, Uncertainty in artificial intelligence Computational Aspects of the Möbius Transformation, Chapter, pp.401-416, 1991.

L. A. Zadeh, On the validity of Dempster's Rule of combination of evidence. Electronics Research Laboratory Memo M 79/24, 1979.

F. Voorbraak, On the justification of Dempster's rule of combination, Artificial Intelligence, vol.48, issue.2, pp.171-197, 1991.
DOI : 10.1016/0004-3702(91)90060-W

J. Dezert, P. Wang, and A. Tchamova, On the validity of Dempster-Shafer Theory, 15th International Conference on Information Fusion (FUSION), pp.655-660, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00720328

C. K. Murphy, Combining belief functions when evidence conflicts, Decision Support Systems, vol.29, issue.1, pp.1-9, 2000.
DOI : 10.1016/S0167-9236(99)00084-6

R. R. Yager, On the dempster-shafer framework and new combination rules, Information Sciences, vol.41, issue.2, pp.93-137, 1987.
DOI : 10.1016/0020-0255(87)90007-7

M. C. Florea, J. Dezert, P. Valin, F. Smarandache, and A. Jousselme, Adaptative combination rule and proportional conflict redistribution rule for information fusion, Proceedings of the COGnitive systems wiith Interactive sensors (COGIS), 2006.

E. Lefevre, O. Colot, and P. Vannoorenberghe, Belief function combination and conflict management, Information Fusion, vol.3, issue.2, pp.149-162, 2002.
DOI : 10.1016/S1566-2535(02)00053-2

A. Martin and C. Osswald, Human expert fusion for image classification Information and Security, Imprecise and Conflicting Information, 2006.
DOI : 10.11610/isij.2006

URL : http://procon.bg/system/files/20.06_ArnaudMartin.pdf

A. Martin and C. Osswald, A new generalization of the proportional conflict redistribution rule stable in terms of decision, Advances and Applications of DSmT for Information Fusion, (Collected Works, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00286568

A. Kallel and S. Le-hegarat-mascle, Combination of partially non-distinct beliefs: The cautious-adaptive rule, International Journal of Approximate Reasoning, vol.50, issue.7, pp.1000-1021, 2009.
DOI : 10.1016/j.ijar.2009.03.006

URL : https://hal.archives-ouvertes.fr/hal-01756255

P. Smets, The concept of distinct evidence Proceedings of the 4th conference on Information Processing and Management of Uncertainty in knowledge-based systems, pp.789-794, 1992.

F. Smarandache, J. Dezert, and J. Tacnet, Fusion of sources of evidence with different importances and reliabilities, 2010 13th International Conference on Information Fusion, pp.1-8, 2010.
DOI : 10.1109/ICIF.2010.5712071

URL : https://hal.archives-ouvertes.fr/hal-00559233

D. Mercier, B. Quost, and T. Denoeux, Refined modeling of sensor reliability in the belief function framework using contextual discounting, Information Fusion, vol.9, issue.2, pp.246-258, 2008.
DOI : 10.1016/j.inffus.2006.08.001

A. Jousselme and P. Maupin, Distances in evidence theory: Comprehensive survey and generalizations, International Journal of Approximate Reasoning, vol.53, issue.2, pp.118-145, 2012.
DOI : 10.1016/j.ijar.2011.07.006

URL : https://doi.org/10.1016/j.ijar.2011.07.006

F. Cuzzolin, A Geometric Approach to the Theory of Evidence, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.38, issue.4, pp.522-534, 2008.
DOI : 10.1109/TSMCC.2008.919174

URL : https://hal.archives-ouvertes.fr/inria-00590222

B. Ristic and P. Smets, The TBM global distance measure for the association of uncertain combat ID declarations, Information Fusion, vol.7, issue.3, pp.276-284, 2006.
DOI : 10.1016/j.inffus.2005.04.004

B. Ristic and P. Smets, Global cost of assignment in the TBM framework for association of uncertain ID reports, Aerospace Science and Technology, vol.11, issue.4, pp.303-309, 2007.
DOI : 10.1016/j.ast.2006.10.008

B. Tessem, Approximations for efficient computation in the theory of evidence, Artificial Intelligence, vol.61, issue.2, pp.315-329, 1993.
DOI : 10.1016/0004-3702(93)90072-J

A. Jousselme, D. Grenier, and É. Bossé, A new distance between two bodies of evidence, Information Fusion, vol.2, issue.2, pp.91-101, 2001.
DOI : 10.1016/S1566-2535(01)00026-4

D. Han, J. Dezert, and Y. Yang, New Distance Measures of Evidence Based on Belief Intervals, Belief Functions: Theory and Applications -Third International Conference, pp.432-441, 2014.
DOI : 10.1007/s11432-011-4541-z

P. Smets, Decision making in the TBM: the necessity of the pignistic transformation, International Journal of Approximate Reasoning, vol.38, issue.2, pp.133-147, 2005.
DOI : 10.1016/j.ijar.2004.05.003

A. Irpino and R. Verde, Dynamic clustering of interval data using a Wasserstein-based distance, Pattern Recognition Letters, vol.29, issue.11, pp.1648-1658, 2008.
DOI : 10.1016/j.patrec.2008.04.008

J. Dezert, D. Han, J. Tacnet, S. Carladous, and Y. Yang, Decision-Making with Belief Interval Distance, Belief Functions: Theory and Applications -4th International Conference, pp.66-74, 2016.
DOI : 10.1016/S1566-2535(01)00026-4

URL : https://hal.archives-ouvertes.fr/emse-01311611

A. Essaid, A. Martin, G. Smits, and B. B. Yaghlane, A Distance-Based Decision in the Credal Level, Artificial Intelligence and Symbolic Computation -12th International Conference, pp.147-156, 2014.
DOI : 10.1007/978-3-319-13770-4_13

URL : https://hal.archives-ouvertes.fr/hal-01110349

P. Smets, Decision Making in a Context where Uncertainty is Represented by Belief Functions, Belief Functions in Business Decisions, pp.17-61, 2002.
DOI : 10.1007/978-3-7908-1798-0_2

URL : http://iridia0.ulb.ac.be/~psmets/DM-BF.ps

N. Wilson, The Assumptions Behind Dempster's Rule, Proceedings of the Ninth International Conference on Uncertainty in Artificial Intelligence, UAI, pp.527-534
DOI : 10.1016/B978-1-4832-1451-1.50069-X

P. Hajek-bouchon-meunier, B. Valverde, L. Yager, and R. R. , Deriving Dempster's rule, Uncertainty in intelligent systems, pp.75-84, 1993.

E. H. Ruspini, The logical foundations of evidential reasoning, 1986.

A. Dromigny-badin, Image fusion using evidence theory: applications to medical and industrial images, 1998.

J. Zhang, Multi-source remote sensing data fusion: status and trends, International Journal of Image and Data Fusion, vol.70, issue.1, pp.5-24, 2010.
DOI : 10.14358/PERS.70.12.1433

URL : https://www.tandfonline.com/doi/pdf/10.1080/19479830903561035?needAccess=true

N. Joshi, M. Baumann, A. Ehammer, R. Fensholt, K. Grogan et al., A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring, Remote Sensing, vol.53, issue.1, p.2016
DOI : 10.1016/j.rse.2011.07.023

M. Turker and M. Arikan, Sequential masking classification of multi???temporal Landsat7 ETM+ images for field???based crop mapping in Karacabey, Turkey, International Journal of Remote Sensing, vol.56, issue.17, pp.3813-3830, 2005.
DOI : 10.1080/014311698216404

T. Fisette, M. Maloley, R. Chenier, L. White, T. Huffman et al., Towards a national agricultural land cover classification-evaluating decision tree approach, Proceedings of the 26th Canadian Symposium on Remote Sensing, pp.14-16, 2005.

J. Inglada, M. Arias, B. Tardy, O. Hagolle, S. Valero et al., Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery, Remote Sensing, vol.1, issue.9, pp.12356-12379, 2015.
DOI : 10.1016/j.rse.2009.03.014

URL : http://www.mdpi.com/2072-4292/7/9/12356/pdf

S. Foerster, K. Kaden, M. Foerster, and S. Itzerott, Crop type mapping using spectraltemporal profiles and phenological information. Computers and Electronics in Agriculture, pp.30-40, 2012.
DOI : 10.1016/j.compag.2012.07.015

URL : http://gfzpublic.gfz-potsdam.de/pubman/item/escidoc:245683:3/component/escidoc:245682/19128.pdf

M. Sandberg, Land cover mapping with multi-temporal SAR and optical satellite data, 2016.

L. D. Pereira, C. D. Freitas, S. J. St-anna, D. Lu, and E. F. Moran, Optical and radar data integration for land use and land cover mapping in the brazilian amazon, GIScience and Remote Sensing, issue.3, pp.50301-321, 2013.

E. Lehmann, Z. Zhou, P. Caccetta, A. Mitchell, A. Milne et al., Combined analysis of optical and sar remote sensing data for forest mapping and monitoring, 7th International Symposium on Digital Earth (ISDE7), 2011.

P. T. Wolter and P. A. Townsend, Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota, Remote Sensing of Environment, vol.115, issue.2, pp.671-691, 2011.
DOI : 10.1016/j.rse.2010.10.010

H. Mcnairn, C. Champagne, J. Shang, D. Holmstrom, and G. Reichert, Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.5, pp.434-449, 2009.
DOI : 10.1016/j.isprsjprs.2008.07.006

T. Idol, B. Haack, and R. Mahabir, Comparison and integration of spaceborne optical and radar data for mapping in Sudan, International Journal of Remote Sensing, vol.68, issue.8, pp.1551-1569, 2015.
DOI : 10.1016/0034-4257(93)90070-E

T. Kavzoglu and P. M. Mather, Pruning artificial neural networks: An example using land cover classification of multi-sensor images, International Journal of Remote Sensing, vol.20, issue.14, pp.2787-2803, 1999.
DOI : 10.1080/014311699211796

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

F. Roli, S. B. Serpico, and L. Bruzzone, Classification of multisensor remote-sensing images by multiple structured neural networks, Proceedings of 13th International Conference on Pattern Recognition, pp.180-184, 1996.
DOI : 10.1109/ICPR.1996.547257

J. Liu, M. Gong, K. Qin, and P. Zhang, A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images, IEEE Transactions on Neural Networks and Learning Systems, vol.29, issue.3, pp.1-15, 2017.
DOI : 10.1109/TNNLS.2016.2636227

M. B. Gibril, S. A. Bakar, K. Yao, M. O. Idrees, and B. Pradhan, Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area, Geocarto International, vol.53, issue.7, pp.32735-748, 2017.
DOI : 10.1080/19475683.2012.730059

J. Betbeder, M. Laslier, T. Corpetti, E. Pottier, S. Corgne et al., Multitemporal optical and radar data fusion for crop monitoring: Application to an intensive agricultural area in brittany(france), 2014 IEEE Geoscience and Remote Sensing Symposium, pp.1493-1496, 2014.
DOI : 10.1109/igarss.2014.6946720

H. Zhang, H. Lin, and Y. Li, Impacts of Feature Normalization on Optical and SAR Data Fusion for Land Use/Land Cover Classification, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.5, pp.1061-1065, 2015.
DOI : 10.1109/LGRS.2014.2377722

C. Oliver and S. Quegan, Understanding synthetic aperture radar images, 1998.

A. Materka and M. Strzelecki, Texture analysis methods ? a review, 1998.

R. Haralick, K. Shanmugam, and I. Dinstein, Texture features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.

Z. Shao, H. Fu, P. Fu, and L. Yin, Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level, Remote Sensing, vol.20, issue.11, p.2016
DOI : 10.1080/2150704X.2014.889861

URL : http://www.mdpi.com/2072-4292/8/11/945/pdf

J. Inglada, A. Vincent, M. Arias, and C. , Marais-Sicre. Improved early crop type identification by joint use of high temporal resolution sar and optical image time series, Remote Sensing, vol.8, issue.5, p.2016

Z. Elouedi, E. Lefevre, and D. Mercier, Discountings of a Belief Function Using a Confusion Matrix, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, pp.287-294, 2010.
DOI : 10.1109/ICTAI.2010.49

J. F. Lemmer, Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence, Proceedings of the First Annual Conference on Uncertainty in Artificial Intelligence (UAI-85), pp.160-176, 1985.
DOI : 10.1016/B978-0-444-70058-2.50013-9

J. Pearl, Reasoning with belief functions: An analysis of compatibility, International Journal of Approximate Reasoning, vol.4, issue.5-6, pp.363-389, 1990.
DOI : 10.1016/0888-613X(90)90013-R

URL : https://doi.org/10.1016/0888-613x(90)90013-r

P. Wang, A Defect in Dempster-Shafer Theory, UAI, pp.560-566, 1994.
DOI : 10.1016/B978-1-55860-332-5.50076-6

URL : http://sites.google.com/site/narswang/publications/wang.D-S.pdf?attredirects=0

A. Gelman, The Boxer, the Wrestler, and the Coin Flip, The American Statistician, vol.60, issue.2, pp.146-150, 2006.
DOI : 10.1198/000313006X106190

J. Dezert and A. Tchamova, On the Validity of Dempster's Fusion Rule and its Interpretation as a Generalization of Bayesian Fusion Rule, International Journal of Intelligent Systems, vol.1, issue.2, pp.223-252, 2014.
DOI : 10.1016/0004-3702(94)90026-4

S. Destercke and D. Dubois, Idempotent conjunctive combination of belief functions: Extending the minimum rule of possibility theory, Information Sciences, vol.181, issue.18, pp.3925-3945, 2011.
DOI : 10.1016/j.ins.2011.05.007

URL : https://hal.archives-ouvertes.fr/hal-00651875

A. Chateauneuf, Combination of compatible belief functions and relations of specificity. Papiers d'economie mathématique et applications, 1992.

M. Cattaneo, Combining belief functions issued from dependent sources, Proceedings of ISIPTA, pp.133-147, 2003.

D. Dubois and R. R. Yager, Fuzzy set connectives as combinations of belief structures, Information Sciences, vol.66, issue.3, pp.245-276, 1992.
DOI : 10.1016/0020-0255(92)90096-Q

D. A. Alvarez, A Monte Carlo-based method for the estimation of lower and upper probabilities of events using infinite random sets of indexable type, Fuzzy Sets and Systems, vol.160, issue.3, pp.384-401, 2009.
DOI : 10.1016/j.fss.2008.08.006

R. R. Yager, Joint cumulative distribution functions for Dempster?Shafer belief structures using copulas. Fuzzy Optimization and Decision Making, pp.393-414, 2013.
DOI : 10.1007/s10700-013-9163-z

B. Schmelzer, Joint distributions of random sets and their relation to copulas, International Journal of Approximate Reasoning, vol.65, pp.59-69, 2015.
DOI : 10.1016/j.ijar.2015.01.007

A. Sklar, Fonctions de répartition à n dimensions et leurs marges, 1959.

A. Sklar, Random variables, joint distributions, and copulas, Kybernetica, vol.9, issue.6, pp.449-460, 1973.

W. Hoeffding, Scale???Invariant Correlation Theory, The Collected Works of Wassily Hoeffding, pp.57-107, 1940.
DOI : 10.1007/978-1-4612-0865-5_4

W. Hoeffding, Scale???Invariant Correlation Measures for Discontinuous Distributions, The Collected Works of Wassily Hoeffding, pp.109-133, 1941.
DOI : 10.1007/978-1-4612-0865-5_5

G. Klir and Y. Bo, Fuzzy Sets and Fuzzy Logic: Theory and Applications, 1995.

D. Dubois and H. Prade, On the use of aggregation operations in information fusion processes. Fuzzy Sets and Systems, pp.143-161, 2004.

H. Joe, Multivariate Models and Multivariate Dependence Concepts. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 1997.
DOI : 10.1201/b13150

H. T. Nguyen, On combining dependent evidence, 2013.

B. Schmelzer, Sklar's theorem for minitive belief functions, International Journal of Approximate Reasoning, vol.63, pp.48-61, 2015.
DOI : 10.1016/j.ijar.2015.05.010

G. Matheron, Random sets and integral geometry, 1975.

O. Wolkenhauer, Data Engineering, 2001.
DOI : 10.1002/0471224340

C. Bertoluzza, M. A. Gil, and D. A. , Ralescu editors. Statistical modeling, analysis and management of fuzzy data of Studies in fuzziness and soft c omputing, 2002.

T. Denoeux, The cautious rule of combination for belief functions and some extensions, 2006 9th International Conference on Information Fusion, pp.1-8, 2006.
DOI : 10.1109/ICIF.2006.301572

G. Beliakov, A. Pradera, and T. Calvo, Aggregation Functions: A Guide for Practitioners, 2008.

C. Alsina, B. Schweizer, and M. J. Frank, Associative Functions: Triangular Norms and Copulas, World Scientific, 2006.
DOI : 10.1142/6036