Abstract
When dealing with unsupervised satellite images classification task, an algorithm such as K-means or ISODATA is chosen to take a data set and find a pre-specified number of statistical clusters in a multispectral space. These standard methods are limited because they require a priori knowledge of a probable number of classes. Furthermore, they also use random principles which are often locally optimal. Several approaches can be used to overcome these problems. In this paper, we are interested in approach inspired by the clustering of corpses and larval observed in real ant colonies. Based on previous works in this research field, we propose an ant-based multispectral image classifier. The main advantage of this approach is that it does not require any information on the input data, such as the number of classes, or an initial partition. Experimental results show the accuracy of obtained maps and so, the efficiency of developed algorithm.
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References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Chretien, L.: Organisation Spatiale du Materiel Provenant de lexcavation du nid chez Messor Barbarus et des Cadavres douvrieres chez Lasius niger (Hymenopterae: Formicidae). PhD thesis, Universite Libre de Bruxelles (1996)
Deneubourg, J.L., Goss, S., Francs, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: Robot-Like Ant and Ant-Like Robot. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings First Conference on Simulation of adaptive Behavior: from animals to animates, pp. 356–365. MIT Press, Cambridge (1991)
Gutowitz, H.: Cellular Automata: Theory and Experiment. MIT Press, Bradford Books (1991)
Handl, J., Meyer, B.: Improved Ant-Based Clustering and Sorting. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 913–923. Springer, Heidelberg (2002)
Kanade, P.M., Hall, L.O.: Fuzzy ants as a clustering concept. In: 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS, pp. 227–232 (2003)
Khedam, R., Outemzabet, N., Tazaoui, Y., Belhadj-Aissa, A.: Unsupervised multispectral classification images using artificial ants. In: IEEE International Conference on Information & Communication Technologies: from Theory to Applications (ICTTA 2006), Damas, Syrie (2006)
Khedam, R., Belhadj-Aissa, A.: Clustering of remotely sensed data using an artificial Ant-based approach. In: The 2nd International Conference on Metaheuristics and Nature Inspired Computing, META 2008, Hammamet, Tunisie (2008)
Khedam, R., Belhadj-Aissa, A.: Cellular Automata for unsupervised remotely sensed data classification. In: International Conference on Metaheuristics and Nature Inspired Computing, Djerba Island, Tunisia (2010)
Kuntz, P., Snyers, D.: Emergent colonization and graph partitioning. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, vol. 3, pp. 494–500. MIT Press, Cambridge (1994)
Le Hégarat-Mascle, S., Kallel1, A., Descombes, X.: Ant colony optimization for image regularization based on a non-stationary Markov modeling. IEEE Transactions on Image Processing (submitted on April 20, 2005)
Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proceedings Third International Conference on Simulation of Adaptive Behavior: from animals to animates, vol. 3, pp. 499–508. MIT Press, Cambridge (1994)
Lumer, E., Faieta, B.: Exploratory database analysis via self-organization (1995) (unpublished manuscript)
Monmarché, N.: On data clustering with artificial ants. In: Freitas, A. (ed.) AAAI 1999 & GECCO-99 Workshop on Data Mining with Evolutionary Algorithms, Research Directions, Orlando, Florida, pp. 23–26 (1999)
Monmarché, N., Slimane, M., Venturini, G.: AntClass: discovery of clusters in numeric data by an hybridization of an ant colony with the K-means algorithm. Technical Report 213, Laboratoire d’Informatique de l’Université de Tours, E3i Tours, p. 21 (1999)
Monmarché, N.: Algorithmes de fourmis artificielles: applications à la classification et à l’optimisation. Thèse de Doctorat de l’université de Tours. Discipline: Informatique. Université François Rabelais, Tours, France, p. 231 (1999)
Ouadfel, S., Batouche, M.: MRF-based image segmentation using Ant Colony System. Electronic Letters on Computer Vision and Image Analysis, 12–24 (2003)
Schockaert, S., De Cock, M., Cornelis, C., Kerre, C.E.: Efficient clustering with fuzzy ants. In: Proceedings Trim Size: 9in x 6in FuzzyAnts, p. 6 (2004)
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Khedam, R., Belhadj-Aissa, A. (2011). Classification of Multispectral Images Using an Artificial Ant-Based Algorithm. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_22
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DOI: https://doi.org/10.1007/978-3-642-21984-9_22
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