Abstract
Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning procedure for deducing a function from training data; unsupervised classification is a kind of classification which no training sample is available and subdivision of the feature space is achieved by identifying natural groupings present in the images values. As a branch of swarm intelligence, ant colony optimization algorithm has self-organization, adaptation, positive feedback and other intelligent advantages. In this paper, ant colony optimization algorithm is tentatively introduced into unsupervised classification of remote sensing images. A series of experiments are performed with remote sensing data. Comparing with the K-mean and the ISODATA clustering algorithm, the experiment result proves that artificial ant colony optimization algorithm provides a more effective approach to remote sensing images classification.
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References
NishiiR, S.: Supervised Images Classification by Contextual Boost Based on Posteriors in Neighborhoods. J. IEEE Transaction on Geoscience and Remote Sensing 43, 2547–2554 (2005)
Shuang, L., Shengyan, D., Shuming, X.: Comparion and research on remote sensing classificision methods. J. Henan University Trans. 32, 70–73 (2002)
Dorigio, M., Colorni, A., Maniezzo, V.: The ant system: optimization by a colony of cooperating agents. J. IEEE Trans. Syst. Man Cybern. B. 26, 29–41 (1996)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. J. IEEE Trans. On Evolutionary Computation 1, 53–66 (1997)
Gambardella, L.M., Taillard, E.D., Dorigo, M.: Ant colonies for the quadratic assignment problem. J. Journal of the Operational Research Society 50, 167–176 (1999)
Maniezzo, V., Colorni, A.: The ant system applied to the quadratic assignment problem. J.IEEE Trans. Knowledge and Data Engineering 11, 769–778 (1999)
Dorigo, M., Dicaro, G.: Ant colony Optimization: A New Meta-heuristic. In: Proc. of 1999 IEEE Congress on Evolutionary Computation Proceedings (CEC 1999), pp. 1470–1477. IEEE Press, Washington (2001)
Zhenglong, W., Rujing, W., Minggui, T., Meisheng, X.: Mining Classification Rule Based on Colony Algorithm. J. Computer Engineering and Application 20, 30–33 (2004)
Shugen, W., Yun, Y., Ying, L., Chonghua, C.: Automatic Classification of Remotely Sensed Images Based on Artificial Ant Colony Algorithm. J. Computer Engineering and Application 29, 77–80 (2005)
Yanfang, H., Pengfei, S.: An improved ant colony algorithm for fuzzy clustering in images segmentation. J. Neurocomputing 70, 665–671 (2007)
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Wang, D., Cheng, B. (2010). An Unsupervised Classification Method of Remote Sensing Images Based on Ant Colony Optimization Algorithm. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_29
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DOI: https://doi.org/10.1007/978-3-642-17316-5_29
Publisher Name: Springer, Berlin, Heidelberg
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