Research on synthetic aperture radar image target recognition based on AdaBoost. ECOC

被引：0
|
作者：
Guo W. [1 ,2 ]
Zhang P. [1 ]
Zhu L. [1 ,2 ]
Chen X. [1 ,2 ]
机构：
[1] Institute of Electronics, Chinese Academy of Sciences
[2] The Graduate School, Chinese Academy of Sciences
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2010年 / 31卷 / 02期
关键词：
AdaBoost; ECOC; Error correcting output code; Synthetic aperture radar; Target recognition;
D O I：
10.3969/j.issn.1006-7043.2010.02.017
中图分类号：
学科分类号：
摘要：
A new method for synthetic aperture radar image target recognition was proposed, which extended the basic AdaBoost algorithm for multi-class classification, and the new algorithm (AdaBoost. ECOC) was applied to synthetic aperture radar image target recognition. The extended algorithm was applied in the recognition experiment on three types of ground military vehicles in MSTAR database and the result was compared with other recognition algorithms. Results were presented to verify that the performance of the recognition system was improved significantly, and the method presented in this paper was an effective method for synthetic aperture radar target recognition.
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页码：232 / 236
页数：4
相关论文
共 10 条
  • [1] Vladimir N., The Nature of Statistical Learning Theory, pp. 157-183, (2000)
  • [2] Oliver C., Quegan S., Understanding Synthetic Aperture Radar Images, pp. 296-315, (1998)
  • [3] Sun Y., Liu Z., Todorovic S., Et al., Adaptive boosting for SAR automatic target recognition, IEEE Transaction on Aerospace and Electronic Systems, 43, 1, pp. 112-125, (2007)
  • [4] Freund Y., Schapire R., A decision-theoretic generalization of on line learning and an application to boosting, Journal of Computer and System Sciences, 55, pp. 119-139, (1997)
  • [5] Kuncheva L., Using diversity measures for generating error-correcting output codes in classifier ensembles, Pattern Recognition Letters, 26, pp. 83-90, (2005)
  • [6] Ling S., Xing C., Coding Theory, pp. 96-99, (2004)
  • [7] Sandirasegaram N., Englisth R., Comparative analysis of feature extraction (2D FFT and wavelet) and classification (Sp metric distances, MLP NN, and HNeT) algorithms for SAR imagery, Proc SPIE, 5808, pp. 314-325, (2005)
  • [8] Mallat S., A Wavelet Tour of Signal Processing, pp. 221-303, (1998)
  • [9] Ross T., Worrell S., Velten V., Et al., Standard SAR ATR evaluation experiments using the MSTAR public release data set, Proc SPIE, 3370, pp. 566-573, (1998)
  • [10] Zhao Q., Principe J.C., Support vector machines for SAR automatic target recognition, IEEE Trans Aerospace and Electronic Systems, 37, 2, pp. 643-654, (2001)
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