M. J. Weissleder, Imaging in the era of molecular oncology, Nature, issue.452, pp.580-589, 2008.

S. A. Culver and W. Akers, Multimodality molecular imaging with combined optical and SPECT/PET modalities, J. Nucl. Med, issue.49, pp.169-172, 2008.

M. Abdullah, Image Acquisition Systems, Comput. Vis. Technol. Food Qual. Eval, pp.3-35, 2008.

V. C. Belin, D. Rousseau, and T. Boureau, Thermography versus chlorophyll fluorescence imaging for detection and quantification of apple scab, Comput. Electron. Agric, vol.90, pp.159-163, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01209914

B. M. Wang, W. Yang, A. Wheaton, and N. Cooley, Efficient registration of optical and IR images for automatic plant water stress assessment, Comput. Electron. Agric, vol.74, issue.2, pp.230-237, 2010.

F. B. Simon-chane, A. Mansouri, and F. S. Marzani, Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives, Image Vis. Comput, vol.31, issue.1, pp.91-102, 2013.

G. Luis, S. Member, D. Tuia, and S. Member, Multimodal Classification of Remote Sensing Images : A Review and Future Directions, pp.1-52, 2015.

A. B. Hanan-anzid-gaetan-le-goic and D. Mammass, Improving point matching on multimodal images using distance and orientation automatic filtering, IEEE/ACS 13th Int. Conf, pp.1-8, 2016.

H. Anzid, G. L. Goic, A. Bekkari, A. Mansouri, and D. Mammass, Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine, Procedia Comput. Sci, vol.148, pp.107-115, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02141064

H. Anzid, G. Le-goic, A. Mansouri, and D. Mammass, Improvement of Multimodal Images Classification Based on DSMT Using Visual Saliency Model Fusion With SVM, Int. J. Comput. Technol, vol.18, pp.7418-7430, 2018.

S. C. Thomopoulos, R. Viswanathan, and D. C. Bougoulias, Optimal decision fusion in multiple sensor systems, IEEE Trans. Aerosp. Electron. Syst, issue.5, pp.644-653, 1987.

A. White, Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3, Nav. Ocean Syst. Center, 1987.

A. Dromigny-badin, Fusion d'images par la théorie de l'évidence en vue d'applications médicales et industrielles, 1998.

P. L. Rothman and R. Denton, Fusion or confusion: knowledge or nonsense?, Data Structures and Target Classification, vol.1470, pp.2-13, 1991.

I. Bloch, Information combination operators for data fusion: a comparative review with classification," in Image and signal processing for Remote Sensing, vol.2315, pp.148-160, 1994.

I. Bloch, Fusion d'informations en traitement du signal et des images, Hermes Sci. Publ, vol.2, 2003.

I. Bloch and H. Maître, Fusion de données en traitement d'images: modèles d'information et décisions, TS. Trait. du signal, vol.11, issue.6, pp.435-446, 1994.

A. Martin, La fusion d'informations, Polycopié de cours ENSIETA-Réf, vol.1484, p.117, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01108256

A. Elhassouny, Fusion d'images par la théorie de Dezert-Smarandache (DSmT) en vue d'applications en télédétection, 2013.

D. Dubois, Théorie des possibilités; applications a la représentation des connaissances en informatique, 1988.

P. Smets, Imperfect information: Imprecision and uncertainty, Uncertainty management in information systems, pp.225-254, 1997.

Y. Yan, Fusion de mesures de déplacement issues d'imagerie SAR: Application aux modélisations séismo-volcaniques, 2011.

E. Lefevre, O. Colot, and P. Vannoorenberghe, Belief function combination and conflict management, Inf. fusion, vol.3, issue.2, pp.149-162, 2002.

F. Smarandache and J. Dezert, Advances and applications of DSmT for information fusion-Collected works, vol.3, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01080187

P. Smets, Varieties of ignorance and the need for well-founded theories, Inf. Sci. (Ny), vol.57, issue.58, pp.135-144, 1991.

C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J, vol.27, issue.3, pp.379-423, 1948.

B. Dasarathy, Sensor fusion potential exploitation-innovative architectures and illustrative applications, Proc. IEEE, vol.85, issue.1, pp.24-38, 1997.

J. Zhang, Multi-source remote sensing data fusion: status and trends, Int. J. Image Data Fusion, vol.1, issue.1, pp.5-24, 2010.

Y. Lemeret, E. Lefevre, and D. Jolly, Fusion de données provenant d'un laser et d'un radar en utilisant la théorie de Dempster-Shafer, MAJECSTIC'04, Fr, 2004.

L. Gómez-chova, D. Tuia, G. Moser, and G. Camps-valls, Multimodal classification of remote sensing images: A review and future directions, Proc. IEEE, vol.103, pp.1560-1584, 2015.

M. Mangolini, T. Ranchin, and L. Wald, Procédé et dispositif pour augmenter la résolution spatiale d'images à partir d'autres images de meilleure résolution spatiale, 1992.

W. Dou, Y. Chen, X. Li, and D. Z. Sui, A general framework for component substitution image fusion: An implementation using the fast image fusion method, Comput. Geosci, vol.33, issue.2, pp.219-228, 2007.

A. Dromigny-badin, Image fusion using evidence theory: applications to medical and industrial images, Thèse EEA. Lyon: INSA de Lyon, p.158, 1998.

I. Hammami, Fusion of heterogeneous remote sensing images by credibilist methods, Ecole nationale supérieure Mines-Télécom Atlantique, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01814777

J. Dong, Z. Dafang, H. Yaohuan, and F. Jinying, Survey of Multispectral Image Fusion Techniques in Remote Sensing Applications, Image Fusion and Its Applications, Yufeng Zheng, 2011.

C. Chu and J. K. Aggarwal, Image interpretation using multiple sensing modalities, IEEE Trans. Pattern Anal. Mach. Intell, issue.8, pp.840-847, 1992.

C. Degrigny, Technical Study of Germolles' wall paintings : the input of imaging techniques, Virtual Archaeol. Rev, vol.7, pp.1-8, 2016.

J. M. Sabater and A. Almansa, How accurate can block matches be in stereovision, SIAM J. Imaging Sci, vol.4, pp.472-500, 2011.

H. Sawhney and R. Kumar, True multi-image alignment and its application to mosaicing and lens distortion correction, PAMI, vol.21, pp.235-243, 1999.

F. A. Leprince, S. Barbot, and J. Avouac, Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements, Commun. Comput. Inf. Sci, vol.127, pp.15-28, 2007.

A. T. Evanthia-faliagka-george-matsopoulos and G. Tzimas, Registration and Fusion Techniques for Medical Images: Demonstration and Evaluation, GRSS, vol.45, pp.1529-1558, 2011.

L. G. Brown, A survey of image registration techniques, ACM Comput. Surv, vol.24, issue.4, pp.325-376, 1992.

J. C. Gee, C. Barillot, L. L. Briquer, D. R. Haynor, and R. K. Bajcsy, Matching structural images of the human brain using statistical and geometrical image features, Visualization in Biomedical Computing, vol.2359, pp.191-205, 1994.

J. Z. Xia and Y. Liu, A robust feature-based registration method of multimodal image using phase congruency and coherent point drift, SPIE, vol.8919, pp.1-8, 2013.

C. Rominger, A. Martin, C. Rominger, and A. Martin, Recalage et fusion d ' images sonar multivues : utilisation du conflit, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00657626

J. W. Zhang and D. Feng, Multi-modal image registration with joint structure tensor and local entropy, Int. J. Comput. Assist. Radiol. Surg, vol.10, pp.1765-1775, 2015.

A. Boucher and L. U. Descartes, Recalage et analyse d'un couple d'images : application aux mammographies, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00798271

J. Chen and J. Tian, Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor, Prog. Nat. Sci, vol.19, pp.643-651, 2008.

A. Sotiras, C. Davatzikos, and N. Paragios, Deformable medical image registration: A survey, IEEE Trans. Med. Imaging, vol.32, issue.7, pp.1153-1190, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00684715

J. N. Ulysses and A. Conci, Measuring Similarity in Medical Registration, 2010.

G. M. Roche and N. Ayache, Unifying Maximum Likelihood Approaches in Medical Image Registration, INRIA, vol.11, pp.71-80, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00072923

B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med. Image Anal, vol.12, issue.1, pp.26-41, 2008.

Q. Z. Xiaoyong-shen-lixu and J. , Multi-modal and multi-spectral registration for natural images, pp.309-324, 2014.

A. S. Konstantinos-karantzalos and N. Paragios, Efficient and automated multimodal satellite data registration through MRFs and linear programming, pp.329-336, 2014.

H. Xin, Modélisation et recalage d'images protéomiques, Institut National des Sciences Appliquées de Lyon, 2008.

D. V. Maes, A. Collignon, and P. Suetens, Multimodality image registration by maximization of mutual information, IEEE Trans. Med. Imaging, vol.16, pp.187-198, 1997.

G. M. Roche and X. Pennec, The correlation ratio as a new similarity measure for multimodal image registration, Proc. Med. Image Comput. Comput. Interv, pp.1115-1124, 1998.
URL : https://hal.archives-ouvertes.fr/cea-00333675

F. Barrera, F. Lumbreras, and A. D. Sappa, Multispectral piecewise planar stereo using Manhattan-world assumption, Pattern Recognit. Lett, vol.34, pp.52-61, 2013.

J. Zhang, J. Wang, X. Wang, and D. Feng, Multimodal image registration with joint structure tensor and local entropy, Int. J. Comput. Assist. Radiol. Surg, vol.10, issue.11, pp.1765-1775, 2015.

C. H. Chen, Signal and image processing for remote sensing, 2007.

R. Bouchiha and K. Besbes, Automatic remote-sensing image registration using SURF, Int. J. Comput. Theory Eng, vol.5, pp.88-92, 2013.

A. Sotiras, Discrete image registration: a hybrid paradigm, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00677442

P. J. Besl and N. D. Mckay, Method for registration of 3-D shapes, Sensor Fusion IV: Control Paradigms and Data Structures, vol.1611, pp.586-607, 1992.

D. G. Lowe, Object recognition from local scale-invariant features, Proc Int. Conf. Comput. Vision, Corfu, vol.5, pp.1150-1157, 1999.

M. B. Firmenichy and S. Susstrunk, Multispectral interest points for RGB-NIR image registration, Proc. IEEE Int. Conf. Image Process, vol.110, pp.181-1849, 2011.

T. T. Bay and L. Gool, SURF: speeded up robust features, Proc. Eur. Conf. Comput. Vis, pp.404-417, 2006.

J. Chen and J. Tian, Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor, Prog. Nat. Sci, vol.19, issue.5, pp.643-651, 2009.

Z. J. Dong-zhao-yan-yang and X. Hu, Rapid multimodality registration based on MM-SURF, Neurocomputing, vol.131, pp.87-97, 2014.

G. Wang, Z. Wang, Y. Chen, and W. Zhao, Biomedical Signal Processing and Control Robust point matching method for multimodal retinal image registration, Biomed. Signal Process. Control, vol.19, pp.68-76, 2015.

R. Roopalakshmi and G. R. Reddy, A novel spatio-temporal registration framework for video copy localization based on multimodal features, Signal Processing, vol.93, issue.8, pp.2339-2351, 2013.

M. I. Patel, V. K. Thakar, and S. K. Shah, Image Registration of Satellite Images with Varying Illumination Level Using HOG Descriptor Based SURF, Procedia Comput. Sci, vol.93, pp.382-388, 2016.

P. Lukashevich, B. A. Zalesky, and S. Ablameyko, Medical image registration based on SURF detector, Pattern Recognit. Image Anal, vol.21, issue.3, p.519, 2011.

M. Su, C. Zhang, Z. Chen, and S. Jiang, Registration of multimodal brain images based on optical flow, Image and Signal Processing, pp.1-5, 2017.

S. Saleem and R. Sablatnig, A robust SIFT descriptor for multi-spectral images, IEEE Signal Process. Lett, vol.21, pp.400-403, 2014.

R. Bajcsy and C. Broit, Matching of deformed images, Sixth International Conference on Pattern Recognition (ICPR'82), pp.351-353, 1982.

G. E. Christensen, R. D. Rabbitt, and M. I. Miller, Deformable templates using large deformation kinematics, IEEE Trans. image Process, vol.5, issue.10, pp.1435-1447, 1996.

G. E. Christensen, Consistent linear-elastic transformations for image matching, Biennial International Conference on Information Processing in Medical Imaging, pp.224-237, 1999.

M. Semchedine, Système coopératif hybride de classification dans un SMA: application à la segmentation d'images IRM, 2018.

S. Susstrunk, R. Buckley, and S. Swen, Standard RGB Color Spaces, IS T/SID 7th Color Imaging Conf, vol.7, 1999.

S. Le-moan, Saliency for spectral image analysis, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, pp.2472-2479, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00841144

M. J. Cubillasa, &. The, . Of, . Support-vector, . Machine-(svm)-using et al., Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, pp.189-194, 2016.

M. Singha and K. Hemachandran, Content based image retrieval using color and texture, Signal Image Process, vol.3, issue.1, p.39, 2012.

P. Mohanaiah, P. Sathyanarayana, and L. Gurukumar, Image texture feature extraction using GLCM approach, Int. J. Sci. Res. Publ, vol.3, issue.5, p.1, 2013.

E. Miyamoto and T. Merryman, Fast calculation of Haralick texture features, Hum. Comput. Interact. institute, 2005.

S. Zhang, J. Huang, Y. Huang, Y. Yu, H. Li et al., Automatic image annotation using group sparsity, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp.3312-3319, 2010.

S. Leutenegger, M. Chli, and R. Y. Siegwart, BRISK: Binary robust invariant scalable keypoints, Computer Vision (ICCV), 2011 IEEE International Conference on, pp.2548-2555, 2011.

A. Alahi, R. Ortiz, and P. Vandergheynst, Freak: Fast retina keypoint, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.510-517, 2012.

D. Kim, Face Components Detection using SURF Descriptors and SVMs, pp.51-56, 2008.

D. Schmitt and N. Mccoy, Object Classification and Localization Using SURF Descriptors, pp.1-5, 2011.

A. Alfanindya, N. Hashim, and C. Eswaran, Content Based Image Retrieval And Classification Using Speeded-Up Robust Features ( SURF ) and Grouped Bag-of-Visual-Words ( GBoVW ), pp.77-82, 2013.

S. Matuska, R. Hudec, P. Kamencay, M. Benco, and M. Zachariasova, Classification of wild animals based on SVM and local descriptors, AASRI Procedia, vol.9, pp.25-30, 2014.

J. Knopp, M. Prasad, G. Willems, R. Timofte, and L. Van-gool, Hough transform and 3D SURF for robust three dimensional classification, European Conference on Computer Vision, pp.589-602, 2010.

F. A. Sharma, Saliency map for human gaze prediction in images, Sixt. Color Imaging Conf, 2008.

C. K. Itti, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, pp.1254-1259, 1988.

Y. T. Chen, An improved saliency detection algorithm based on Itti's model, Teh. Vjesn, pp.1337-1344, 2014.

K. N. Han, Unsupervised extraction of visual attention objects in color images, Trans Circuits Syst. Video Technol, pp.141-145, 2006.

D. W. Rutishauser, Is bottom-up attention useful for object recognition?, pp.37-44, 2004.

F. W. Wei, Geodesic saliency using background priors, 2012.

V. , Saliency-based discriminant tracking, 2009.

C. G. Zhang, A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression, IEEE Trans. Image Process, pp.185-198, 2010.

I. Koch, A saliency-based search mechanism for overt and covert shifts of visual attention, Vision Res, 2000.

A. O. Torralba, Contextual guidance of attention and eye movements in real-world scenes: the role of global features in object search, Psychol. Rev, 2006.

K. E. Judd, Learning to predict where humans look, IEEE 12th Int. Conf. Comput. Vis, pp.2106-2113, 2009.

A. Torralba, Modeling global scene factors in attention, JOSA, 2003.

X. H. Zhan, Saliency Detection: A Spectral approach, ieee Conf. Comput. Vis. pattern Recognit, 2007.

N. Bruce and J. Tsotsos, Saliency based on information maximization, Advances in neural information processing systems, pp.155-162, 2006.

J. Harel, Graph-based visual saliency, Adv. Neural Inf. Process. Syst, pp.545-552, 2007.

S. He, Exemplar-driven top-down saliency detection via deep association, Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp.5723-5732, 2016.

C. S. Francesca-murabito, Top-Down Saliency Detection Driven by Visual Classification, pp.1709-5307, 2017.

R. Bharath, Scalable scene understanding using saliency-guided object localization, Control Autom. (ICCA), 2013 10th IEEE Int, pp.1503-1508, 2013.

X. Hou, Image signature: Highlighting sparse salient regions, IEEE Trans. Pattern Anal. Mach. Intell, pp.194-201, 2012.

S. and .. Goferman, Context-aware saliency detection, IEEE Trans. Pattern Anal. Mach. Intell, pp.1915-1926, 2012.

R. E. , Segmenting salient objects from images and videos, Eur. Conf. Comput. Vis, pp.366-379, 2010.

L. Wald, Some terms of reference in data fusion, IEEE Trans. Geosci. Remote Sens, vol.37, issue.3, pp.1190-1193, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00356150

J. J. Clarke and A. L. Yuille, Data fusion for sensory information processing, Boston, MA Kluwer Acad. doi, vol.10, pp.971-978, 1990.

L. A. Zadeh, Probability measures of fuzzy events, J. Math. Anal. Appl, vol.23, issue.2, pp.421-427, 1968.

G. Shafer, A mathematical theory of evidence, vol.42, 1976.

F. Smarandache and J. Dezert, Applications and Advances of DSmT for Information Fusion, Collected Works, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01080187

A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, Classic Works of the Dempster-Shafer Theory of Belief Functions, pp.57-72, 2008.

A. Samet, E. Lefevre, and S. Ben-yahia, Belief function classification with conflict management: application on forest image, Signal-Image Technology and Internet-Based Systems (SITIS), pp.14-20, 2014.

C. Lian, Information fusion and decision-making using belief functions: application to therapeutic monitoring of cancer, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01535758

I. Hammami, Fusion of heterogeneous remote sensing images by credibilist methods Imen Hammami, 2018.

L. Comtet, Advanced Combinatorics: The Art of Finite and Infinite Expansions, Math. Rev. MR460128 Zentralblatt MATH, vol.283, 1974.

R. Dedekind, Über Zerlegungen von Zahlen durch ihre grössten gemeinsamen Theiler, Fest-Schrift der Herzoglichen Technischen Hochschule Carolo-Wilhelmina, pp.1-40, 1897.

G. Quellec, Multimodal medical case retrieval using the dezertsmarandache theory, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00331184

P. Smets and R. Kennes, The transferable belief model, Artif. Intell, vol.66, issue.2, pp.191-234, 1994.
URL : https://hal.archives-ouvertes.fr/hal-01185821

P. Smets, Data fusion in the transferable belief model, Proceedings of the Third International Conference on, vol.1, pp.21-33, 2000.

D. Dubois and H. Prade, Fuzzy sets and systems: Theory and applications, Am. Math. Soc, vol.7, issue.3, pp.603-612, 1982.

R. R. Yager, On the Dempster-Shafer framework and new combination rules, Inf. Sci. (Ny), vol.41, issue.2, pp.93-137, 1987.

R. R. Yager, Hedging in the combination of evidence, J. Inf. Optim. Sci, vol.4, issue.1, pp.73-81, 1983.

J. Dezert and F. Smarandache, Proportional conflict redistribution rules for information fusion, Adv. Appl. DSmT Inf. Fusion-Collected Work, vol.2, pp.3-68, 2006.

P. Djiknavorian and D. Grenier, Reducing DSmT hybrid rule complexity through optimization of the calculation algorithm, Adv. Appl. DSmT Inf. Fusion, p.365, 2006.

A. Nassim, Développement de modèles de fusion et de classification contextuelle d'images satellitaires par la théorie de l'évidence et la théorie du raisonnement plausible et paradoxal, 2009.

A. Elhassouny, S. Idbraim, A. Bekkarri, D. Mammass, and D. Ducrot, Multisource Fusion / Classification Using ICM and DSmT with New Decision Multisource Fusion / Classification Using ICM and DSmT with New Decision Rule, 2012.

F. Smarandache and J. Dezert, Advances and Applications of DSmT for Information Fusion, Vol. IV: Collected Works". Infinite Study, 2015.

T. Lee, J. A. Richards, and P. H. Swain, Probabilistic and evidential approaches for multisource data analysis, IEEE Trans. Geosci. Remote Sens, issue.3, pp.283-293, 1987.

E. Zahzah, Contribution à la représentation des connaissances et à leur utilisation pour l'interprétation automatique des images satellite, vol.3, 1992.

H. Kim and P. H. Swain, Evidential reasoning approach to multisource-data classification in remote sensing, IEEE Trans. Syst. Man. Cybern, vol.25, issue.8, pp.1257-1265, 1995.

N. Milisavljevi? and I. Bloch, Improving mine recognition through processing and Dempster-Shafer fusion of multisensor data, Comput. Intell. Recognition, Tech. Appl, vol.17, pp.319-343, 2005.

A. Jousselme, É. Bossé, and A. Jouan, Analysing an identity information fusion algorithm based on evidence theory, DEFENCE RESEARCH AND DEVELOPMENT CANADAVALCARTIER (QUEBEC), 2004.

H. Chu, X. Guisong, and S. Hong, SAR images classification method based on Dempster-Shafer theory and kernel estimate, J. Syst. Eng. Electron, vol.18, issue.2, pp.210-216, 2007.

I. Hammami, J. Dezert, and G. Mercier, Kohonen-Based Credal Fusion of Optical and Radar Images for Land Cover Classification, 2018 21st International Conference on Information Fusion (FUSION), pp.1623-1630, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02335697

M. Liu and S. Chen, SAR target configuration recognition based on the Dempster-Shafer theory and sparse representation using a new classification criterion, Int. J. Remote Sens, pp.1-19, 2019.

F. Yang, H. Wei, and P. Feng, A hierarchical Dempster-Shafer evidence combination framework for urban area land cover classification, Measurement, 2018.

W. Ouerghemmi, A. L. Bris, N. Chehata, and C. Mallet, A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.42, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02384455

H. A. Moghaddam and S. Ghodratnama, Toward semantic content-based image retrieval using Dempster-Shafer theory in multi-label classification framework, Int. J. Multimed. Inf. Retr, vol.6, issue.4, pp.317-326, 2017.

R. Khedam, Improvement of Land Cover Map from Satellite Imagery using DST and DSmT Improvement of Land Cover Map from Satellite Imagery Using DST and DSmT, 2006.

C. Lian, S. Ruan, T. Denoeux, and P. Vera, Outcome prediction in tumour therapy based on Dempster-Shafer theory, IEEE 12th International Symposium on, pp.63-66, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01152874

C. Lian, S. Ruan, T. Denoeux, F. Jardin, and P. Vera, Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction, Med. Image Anal, vol.32, pp.257-268, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01324553

S. Razi, M. R. Karami, and J. Ghasemi, A novel method for classification of BCI multiclass motor imagery task based on Dempster-Shafer theory, Inf. Sci, 2019.

I. Bloch, Some aspects of Dempster-Shafer evidence theory for classification of multimodality medical images taking partial volume effect into account, Pattern Recognit. Lett, vol.17, issue.8, pp.905-919, 1996.

Y. M. Zhu, L. Bentabet, O. Dupuis, V. Kaftandjian, D. Babot et al., Automatic determination of mass functions in Dempster-Shafer theory using fuzzy cmeans and spatial neighborhood information for image segmentation, Opt. Eng, vol.41, issue.4, pp.760-771, 2002.

M. Wafa and E. Zagrouba, Tumor extraction from multimodal MRI, Computer Recognition Systems, vol.3, pp.415-422, 2009.

S. Corgne, L. Hubert-moy, J. Dezert, and G. Mercier, Land cover change prediction with a new theory of plausible and paradoxical reasoning, Proc. of Fusion, pp.8-11, 2003.

A. Bouakache, A. Belhadj-aissa, and G. Mercier, Satellite image fusion using Dezert-Smarandache theory, 2009.

A. Bouakache, Fusion des images satellitaires par la théorie d'évidence et la théorie du raisonnement plausible at paradoxal, 2005.

A. Elhassouny, S. Idbraim, A. Bekkari, D. Mammass, and D. Ducrot, Application of DSmT-ICM with Adaptive decision rule to supervised classification in multisource remote sensing, JOURNAL OF COMPUTING, vol.5, issue.1, 2013.

A. Elhassouny, S. Idbraim, and D. Ducrot, Change Detection by Fusion / Contextual Classification based on a Hybrid DSmT Model and ICM with Constraints Change Detection by Fusion / Contextual Classification based on a Hybrid DSmT Model and ICM with Constraints, 2011.

J. Okaingni, S. Ouattara, A. Kouassi, W. J. Vangah, A. K. Koffi et al., Modeling and Characterization of Vegetation, Aquatic and Mineral Surfaces Using the Theory of Plausible and Paradoxical Reasoning from Satellite Images: Case of the Toumodi-Yamoussoukro-Tiébissou Zone in V Baoulé, 2017.

B. Pannetier and J. Dezert, Multiple ground target tracking and classification with DSmT, Adv. Appl. DSmT Inf. Fusion, p.337, 2015.

G. Quellec, M. Lamard, G. Cazuguel, C. Roux, and B. Cochener, Multimodal medical case retrieval using the dezert-smarandache theory, 30th Annual International Conference of the IEEE, pp.394-397, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00331184

A. Jousselme, A. Martin, and P. Maupin, Gestion de l'information paradoxale contrainte par des requêtes pour la classification de cibles dans un réseau de capteurs multi-modalités, Colloque Systèmes Complexes d'Information et de Gestion des Risques pour l'Aide à la Décision, pp.24-25, 2008.

N. Abbas, The effective use of the DSmT for multi-class classification, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01558062

N. Abbas, Y. Chibani, B. Hadjadji, and Z. A. Omar, A DSmT Based System for Writer-Independent Handwritten Signature Verification, 19th International Conference on Information Fusion (FUSION). IEEE, 2016.

J. M. Pluim and M. Viergever, Image registration by maximization of combined mutual information and gradient information, Proc. Med. Image Comput. Comput. Interv, pp.103-129, 2000.

&. B. Fischler, M. A. , and R. , Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, pp.381-395, 1981.

, Robust outliers detection in image point matching, Comput. Vis. Work. (ICCV Work. IEEE Int. Conf, pp.180-187, 2011.

H. Anzid, G. L. Goic, A. Bekkari, A. Mansouri, and D. Mammass, An automatic filtering algorithm for SURF-based registration of remote sensing images, Advanced Technologies for Signal and Image Processing, pp.1-7, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01858199

V. N. Vapnik, An overview of statistical learning theory, IEEE Trans. neural networks, vol.10, issue.5, pp.988-999, 1999.

L. Chapel, Maintenir la viabilité ou la résilience d'un système : les machines à vecteurs de support pour rompre la malédiction de la di-mensionnalité, 2007.

S. Aseervatham, Apprentissage à base de Noyaux Sémantiques pour le traitement de données textuelles, 2007.

R. G. Congalton, A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens. Environ, vol.37, issue.1, pp.35-46, 1991.

A. Bekkari, S. Idbraim, A. Elhassouny, D. Mammass, M. E. Yassa et al., Spectral and Spatial Classification of High Resolution Urban Satellites Images using Haralick features and SVM with SAM and EMD distance Metrics, vol.46, pp.28-37, 2012.

M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, Advances in spectral-spatial classification of hyperspectral images, Proc. IEEE, vol.101, issue.3, pp.652-675, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00737075

T. T. Herbert-bay, A. Ess, and L. Van-gool, Speeded-up robust features (SURF), Comput. Vis. image Underst, vol.110, issue.3, pp.346-359, 2008.

A. I. Peters, Components of bottom-up gaze allocation in natural images, Vision Res, pp.2397-2416, 2005.

J. Yu, A computational model for object-based visual saliency: Spreading attention along gestalt cues, IEEE Trans. Multimed, pp.273-286, 2016.

X. Hou, Saliency detection: A spectral residual approach, Comput. Vis. Pattern Recognition, CVPR'07, pp.1-8, 2007.

E. Rahtu, J. Kannala, M. Salo, and J. Heikkil, Segmenting Salient Objects from Images and Videos, Eur. Conf. Comput. Vis, pp.366-379, 2010.

C. Samson, Contribution à la classification des images satellitaires par approche variationnelle et équation aux dérivées partielles, 2000.

S. F. Gull, Developments in maximum-entropy data analysis, Maximum Entropy and Bayesian Methods, pp.53-71, 1989.

P. Kamavisdar, S. Saluja, and S. , A survey on image classification approaches and techniques, Int. J. Adv. Res. Comput. Commun. Eng, vol.2, issue.1, pp.1005-1009, 2013.

J. Weston and C. Watkins, Multi-class support vector machines, 1998.

J. H. Friedman, Another approach to polychotomous classification, 1996.

V. N. Vapnik, Estimation of dependencies based on empirical data Springer, 1982.

O. Bousquet, Introduction au Support Vector Machines (SVM), Cent. Math. applied, Polytech. Sch. Palaiseau, 2001.

A. Bekkari, M. El, and S. Idbraim, SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information, 2013.

S. Majumdar and J. Bharadwaj, Feature Level Fusion of Multimodal Images Using Haar Lifting Wavelet Transform, World Acad. Sci. Eng. Technol. Int. J. Comput. Electr

, Autom. Control Inf. Eng, pp.1023-1027, 2014.

A. E. , Aissam Bekkari Soufian Idbraim, "spectral and spatial information classification of high resolution urban satelltes Images using haralick features and svm with sam and EMD distance metrics, Int. J. Comput. Appl, pp.975-8887, 2012.

S. Knerr, L. Personnaz, and G. Dreyfus, Single-layer learning revisited: a stepwise procedure for building and training a neural network, Neurocomputing, pp.41-50, 1990.

A. Bekkari, S. Idbraim, A. Elhassouny, D. Mammass, and D. Ducrot, Svm and Haralick features for classification of high resolution satellite images from urban areas, International Conference on Image and Signal Processing, pp.17-26, 2012.

J. G. Licciardi, F. Pacifici, D. Tuia, S. Prasad, T. West et al., Decision fusion for the classification of hyperspectral data, IEEE Trans. Geos. Remote Sens, vol.47, issue.11, pp.3857-3865, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00449440

D. A. Landgrebe, Hyperspectral image data analysis, IEEE Signal processing magazine, vol.19, issue.1, pp.17-28, 2002.

J. Al-doski, S. B. Mansorl, and H. Z. Shafri, Image classification in remote sensing, Dep. Civ. Eng. Fac. Eng. Univ. Putra, 2013.

L. Yu, A. Porwal, E. Holden, and M. C. Dentith, Towards automatic lithological classification from remote sensing data using support vector machines, Comput. Geosci, vol.45, pp.229-239, 2012.

L. Wu, Y. Wang, and J. Long, An Unsupervised Change Detection Approach for Remote Sensing Image Using SURF and SVM, Chinese Conference on Pattern Recognition, pp.537-551, 2016.

J. Dukart, Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI, Psychiatry Res. Neuroimaging, vol.212, issue.3, pp.230-236, 2013.

C. Hinrichs, Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset, Neuroimage, vol.48, issue.1, pp.138-149, 2009.

D. Zhang, Y. Wang, L. Zhou, H. Yuan, D. Shen et al., Multimodal classification of Alzheimer's disease and mild cognitive impairment, Neuroimage, vol.55, issue.3, pp.856-867, 2011.

J. Dukart, Combined evaluation of FDG-PET and MRI improves detection and differentiation of dementia, PLoS One, vol.6, issue.3, p.18111, 2011.

J. F. Molina, L. Zheng, M. Sertdemir, D. J. Dinter, S. Schönberg et al., Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma, PLoS One, 2014.

Z. Zhou, Y. Du, N. L. Thomas, and E. J. Delp, Multimodal eye recognition, Mob. Multimedia/Image Process. Secur. Appl, pp.770-806, 2010.

G. S. Kumar and C. J. Devi, A Multimodal SVM Approach for Fused Biometric Recognition, Int. J. Comput. Sci. Inf. Technol, pp.3327-3330, 2014.

A. Apatean, A. Rogozan, and A. Bensrhair, Svm-based obstacle classification in visible and infrared images, pp.293-297, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00441455

R. Brehar, C. Vancea, and T. Marit, Pedestrian Detection in the Context of Multiple-Sensor Data Alignment for Far-Infrared and Stereo Vision Sensors, 2015 IEEE International Conference on Intelligent Computer Communication and Processing, pp.385-392, 2015.

C. Pomrehn, D. Klein, A. Kolb, P. Kaul, and R. Herpers, Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison, Chemom. Intell. Lab. Syst, 2018.

Y. Mustafa, K. M. Clawson, and C. Bowerman, Saving Cultural Heritage with Digital Make-Believe : Machine Learning and Digital Techniques to the Rescue, 2017.