Abstract
In this paper, we shall describe an evidential supervised classifier of multispectral satellite images. The evidence theory of Dempster-Shafer (DST) is used to take into account the ignorance and the uncertainty related to data, and so, overcome the Bayesian classifier limits. Notice that application fields of DST are initially related on multisensor, multitemporal and multiscale data fusion. In this study, our contribution lies in developing an evidential classification process that can be seen as a multisource fusion process where each predefined thematic class is considered as one source of information. The evidential mass functions of the considered thematic hypotheses are estimated using Appriou’s transfer model whose we propose to generalize to a multi-class case. Developed DST-classifier has been tested on multispectral ETM+ image covering the urban north-eastern part of Algiers (Algeria). The spectral validation of obtained evidential classes allows us to confirm the accuracy of the resulting land cover map.
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References
Appriou, A.: Probabilités et incertitude en fusion de données multi-senseurs. Revue Scientifique et Technique de la Défense 11, 27–40 (1991)
Appriou, A.: Multisensor signal processing in the framework of the theory of evidence. In: NATO/RTO, Application of Mathematical Signal Processing Techniques to Mission Systems. ONERA, Toulouse, France (1999)
Bendjabour, A., Pieczenski, W.: Unsupervised Image Segmentation Using Dempster-Shafer Fusion in a Markov Fields Context. In: First International Conference on Multisource-Multisensor Information Fusion, Las Vegas, Nevada, USA, pp. 595–600 (1998)
Bloch, I.: Information Combination Operators for Data Fusion: A comparative review with classification. IEEE, Trans. Sys. Man Cybern. A 26, 52–67 (1996)
Bloch, I.: Fusion d’informations en traitement du signal et des images. Edition Hermès Science, Paris, France, p. 319 (2003)
Celleux, G., Diday, E., Govaert, G., Lechevallier, Y., Ralambondrainy, H.: Classification automatique des données. Editions Dunod Informatique, Paris, France (1989)
Chatalic, P.: Raisonnement déductif en présence de connaissances imprécises et incertaines: Un système basé sur la théorie de Dempster-Shafer. Thèse Phd, Université Paul Sabatier, Toulouse, France, p. 357 (1986)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38(2), 325–339 (1967)
Denoeux, T.: Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition 30(7), 1095–1107 (1997)
Dubois, D., Prade, H.: A Set-Theoretic View on Belief Functions: Logical Operations and Approximations by Fuzzy Sets. International Journal of General Systems 12, 193–226 (1986)
Dubois, D., Prade, H.: Possibility theory, an approach to the computerized processing of uncertainty. Plenum Press, New York (1988)
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. J. Wiley & Sons, Chichester (1973)
Khedam, R., Bouakache, A., Mercier, G., Belhadj-Aissa, A.: Fusion multitemporelle par la théorie de Dempster-Shafer pour la détection et la cartographie des changements. Application au milieu urbain et périurbain de la région d’Alger. Revue Télédétection 6(4), 359–404 (2006)
Le Hégarat-Mascle, S., Bloch, I., Vidal-Madjar, D.: Application of Dempster Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Transactions on Geosciences and Remote Sensing 35(4), 1018–1031 (1997)
Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis. An Introduction, 3rd edn. Springer, Berlin (1998)
Shafer, G.: A Mathematical Theory of Evidence, p. 312. Princeton University Press, Princeton (1976)
Smarandache, F., Dezert, J.: Advances and Application of DSmT for Information Fusion, p. 418. American Research Press (2004)
Smets, P.: Constructing the pignistic probability function in a context of uncertainty. In: Proceedings of the 5th Annual Conference on Uncertainty in Artificial Intelligence UAI 1989, Windsor, Ontario, pp. 319–326. North Holland Publishing Co., Amsterdam (1989)
Smets, P.: The Combination of Evidence in the Transferable Belief Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 447–458 (1990)
Vannoorenberghe, P.: Un état de l’art sur les fonctions de croyance appliquées au traitement de l’information. Rapport technique, CNRS, Université de Rouen, UFR des Sciences. Revue 13 20(20) (2004)
Zadeh, L.A.: Fuzzy algorithm. Inform. Contr. 12, 94–102 (1968)
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Bouakache, A., Khedam, R., Belhadj-Aissa, A., Mercier, G. (2008). A Generalized Appriou’s Model for Evidential Classification of Multispectral Images: A Case Study of Algiers City. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_81
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DOI: https://doi.org/10.1007/978-3-540-88458-3_81
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