Abstract
Remote sensing is the most relevant science that permits us to acquire information about the surface of the land, without having actual contact with the area being observed. Amongst the multiple uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Multi spectral classification of remotely sensed data has been widely used to generate thematic Land-Use/Land-Cover maps. Two of the extensively used algorithms for image classification are Self Organizing Feature Maps (SOFM) and Ant Colony Optimization. Although both are bio-inspired optimization techniques, however combining them is a challenging task, especially in the field of remote sensing. In this paper, we have used a Self Organizing Ant Algorithm for Classification of remotely sensed data. Also, we have suggested a new reinforcement factor for the pheromone updation. A test of algorithm is conducted by classifying a high resolution, multi-spectral satellite image of Alwar Region. Results obtained are encouraging.
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References
Lu, D., Weng, Q.: A Srvey of Image Classification Methods and Techniques for improving classification performance. International Journal of Remote Sensing 28(5), 823–870 (2007)
Mather, P., Tso, B.: Classification Methods for Remotely Sensed Data, 2nd edn. CRC Press, Boca Raton (2009)
Khedam, Outemzabet, R., Tazaoui, N., Belhadj-Aissa, Y.: A Unsupervised Multispectral Image Classification using Artificial Ants. In: Information and Communication Technologies, ICTTA 2006, Fac. of Electron. and Comput. Sci., Univ. of Sci. and Technol. Houari Boumediene, Algiers, pp. 349–354 (2006)
Omkar, Manoj, S.N., Mudigere, K.M., Dipti Muley, D.: Urban Satellite Image Classification using Biologically Inspired Techniques. In: IEEE International Symposium on Industrial Electronice, ISIE 2007, June 4-7, pp. 1767–1772 (2007)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: actes de la première conférence européenne sur la vie artificielle, Paris, France, pp. 134–142. Elsevier Publishing, Amsterdam (1991)
Kennedy, J., Eberhart, R.: Proc. IEEE Int’l. Conf. on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Fernandes, C., Mora, A.M., Merelo, J.J., Ramos, V., Laredo, J.L.J.: Kohonants: A self organizing ant algorithm for clustering and pattern classification. In: Artificial Life XI 2008 (2008)
Chandramouli, K.: Particle Swarm Optimisation and Self Organising Maps based Image classifier. In: Second International Workshop on Semantic Media Adaptation and Personalization. IEEE, Los Alamitos (2007)
Kohonen, T.: Self organizing maps. Springer, New York (2001)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)
Chialvo, D., Millonas, M.: How Swarms build cognitive maps. In: The Biology and Technology of Intelligent Autonomous Agents. NATO ASI Series, vol. 144, pp. 439–450 (1995)
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Goel, S., Sharma, A., Panchal, V.K. (2011). A Hybrid Algorithm for Satellite Image Classification. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication and Control. ICAC3 2011. Communications in Computer and Information Science, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18440-6_41
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DOI: https://doi.org/10.1007/978-3-642-18440-6_41
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