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SAR Image Classification Based on Clonal Selection Algorithm

  • Wenping Ma
  • Ronghua Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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.

Keywords

Cluster Center Synthetic Aperture Radar Clonal Selection Synthetic Aperture Radar Image Artificial Immune System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wenping Ma
    • 1
  • Ronghua Shang
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anP.R. China

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