Abstract
A method for SAR image recognition algorithm is proposed, which makes use of the global optimal search ability and the quick local search ability of Immune Clonal Programming (ICP) [1] to obtain the prototype vectors in Synergetic Neural Networks (SNN) [2]. As a result, the recognition performance of SNN is improved. Moreover, a study has been made of multi-class recognition using SNN, a bottleneck problem of SNN, and the strategy of One-Against-One [3] is introduced in this paper. Simulation result shows the recognition accuracy rate of SNN is satisfied.
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© 2004 Springer-Verlag Berlin Heidelberg
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Gou, S., Jiao, L. (2004). SAR Image Recognition Using Synergetic Neural Networks Based on Immune Clonal Programming. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_151
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DOI: https://doi.org/10.1007/978-3-540-28647-9_151
Publisher Name: Springer, Berlin, Heidelberg
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