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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
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
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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Mr. Faiza DAKKA
- Monaco
dakka_f@yahoo.fr
Mr. Ahmed HAMMOUCH
- Morocco
Mr. Driss ABOUTAJDINE
- Morocco
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