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Unsupervised multispectral image Classification By fuzzy hidden Markov chains model For SPOTHRV Images
Faiza DAKKA, Ahmed HAMMOUCH, Driss ABOUTAJDINE
Pages - 446 - 455     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
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KEYWORDS
Bayesian Image Classification, Markov Chains, Fuzzy Hidden Markov
ABSTRACT
This paper deals with unsupervised classification of multi-spectral images, we propose to use a new vectorial fuzzy version of Hidden Markov Chains (HMC). The main characteristic of the proposed model is to allow the coexistence of crisp pixels (obtained with the uncertainty measure of the model) and fuzzy pixels (obtained with the fuzzy measure of the model) in the same image. Crisp and fuzzy multi-dimensional densities can then be estimated in the classification process, according to the assumption considered to model the statistical links between the layers of the multi-band image. The efficiency of the proposed method is illustrated with a Synthetic and real SPOTHRV images in the region of Rabat. The comparisons of two methods: fuzzy HMC and HMC are also provided. The classification results show the interest of the fuzzy HMC method.
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5 PdfSR 
A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Roy. Stat. Soc. 39(1), 1–38 (1977).
B. Benmiloud and W. Pieczynski. Estimation des paramètres dans les CMC et segmentation d'images. Traitement du Signal, 12(5) :433{454, 1995.
H. Caillol, W. Pieczynski, A. Hillion. Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation. IEEE Trans. on Im. Proc., 6(3) : 425- 440, 1997.
L. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains,” Ann. Math. Stat. 41(1), 164–171 (1970).
L. R. Rabiner, “A tutorial on HMMs and selected applications in speech recognition,” Proc. IEEE 77(2), 257–286 (1989). [doi:10.1109/5.18626].
L. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” in Proc. IEEE, vol. 77, no. 2, Feb. 1989, pp. 257–286.
N. Giordana and W. Pieczynski, “Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell.19(5), 465–475 (1997). [doi:10.1109/34.589206].
P. A. Devijver, “Baum’s forward-backward algorithm revisited,” Pattern Recognition Letters, vol. 39, no. 3, pp. 369–373, december 1985.
P.-A. Devijver, “Baum’s Forward-Backward algorithm revisited,” Pattern Recogn. Lett. 3,369–373 (1985).
S. Derrode, C. Carincotte, and J.M. Boucher. Unsupervised image segmentation based on high-order hidden Markov chains. In IEEE ICASSP, Montreal (Ca), 17-21 mai 2004.
S. Derrode, G. Mercier, and W. Pieczynski, “Unsupervised change detection in SAR images using a multicomponent HMC model,” in MultiTemp’03, (Ispra, Italy) (2003).
W. Pieczynski, “Statistical image segmentation,” Mach. Graph. and Vis.,vol. 1, pp. 261– 268, 1992.
W. Pieczynski. Mod?eles de Markov en traitement d'images. Traitement du Signal, 20(3) :255{278, 2003.
W. Pieczynski. Statistical image segmentation. Mach. Graph. and Vis., 1 : 261-268, 1992.
W. Skarbek. Generalized Hilbert scan in image printing. In R. Klette and W. G. Kropatsch, editors, Theoretical Foundations of Computer Vision. Akademik Verlag, Berlin, 1992.
Mr. Faiza DAKKA
- Monaco
dakka_f@yahoo.fr
Mr. Ahmed HAMMOUCH
- Morocco
Mr. Driss ABOUTAJDINE
- Morocco