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Unsupervised Multispectral Image Classification using Artificial Ants

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4 Author(s)
Khedam, R. ; Fac. of Electron. & Comput. Sci., Univ. of Sci. & Technol. Houari Boumediene, Algiers ; Outemzabet, N. ; Tazaoui, Y. ; Belhadj-Aissa, A.

Based on the existing works dealing on data clustering with artificial ants, we contribute in this paper to resolve a real clustering problem related on unsupervised multispectral image classification using ants approach, where classes are found without the a priori knowledge of the correct number of classes. Knowing that most of the unsupervised classification methods require the definition of a probable number of classes and an initial partition, the proposed ant-based approach is very interesting insofar for remotely sensed data over the whole of earth, it is not easy to obtain this a priori knowledge

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Information and Communication Technologies, 2006. ICTTA '06. 2nd  (Volume:1 )

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