Abstract
This paper presents a new classification method based on the Clonal Selection Principle, named Clonal Selection Algorithm (CSA). The new algorithm can carry out the global search and the local search in many directions rather than one direction around the same antibody simultaneously, and obtain the global optimum quickly. The implementation of new algorithm composes of three main processes: firstly, selecting training samples and choosing clustering centers randomly. Secondly, training samples using CSA, and obtaining optimal clustering center based on three main clonal operations: cloning, clonal mutation and clonal selection. Finally, output the classification results according to clustering center obtained. To show the usefulness of this approach, experiment with simulated SAR image was considered. The classification results are evaluated by comparing with three well-known algorithms, UAIC, K-means, and fuzzy K-means. Accroding to the overall accuracy and Kappa coefficient, CSA has high classification precision and can be used in SAR images classification.
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Ma, W., Shang, R. (2006). SAR Image Classification Based on Clonal Selection Algorithm. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_122
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DOI: https://doi.org/10.1007/11881070_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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