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Target recognition for small samples of ladar range image using classifier ensembles

机译:Target recognition for small samples of ladar range image using classifier ensembles

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摘要

The range image has received considerable attention in the automatic target recognition field; however, a mass of range images are generally difficult to be collected in a real application. Therefore, with small samples of laser radar (ladar) range images, classifier ensembles-support vector machine (SVM) ensembles and back propagation neural networks (BPNN) ensembles--are applied to improve the performance of target recognition in this paper. There are three aspects of experiments. First, the performances of SVM and BPNN are compared with different numbers of training sets. Secondly, the SVM ensembles and the BPNN ensembles are applied to improve the performance. Thirdly, the performances of the SVM ensembles and the BPNN ensembles are analyzed with the view angle of the tested samples changing while the view angle of the trained samples is invariant. The experimental results demonstrate that the recognition rate is effectively improved by classifier ensembles. The SVM is superior to the BPNN when the number of the training sets is not less than the feature dimensions; however, the BPNN has a better approximating ability when the number of training sets is small and lower than feature dimensions.

著录项

  • 来源
    《Optical Engineering》 |2012年第8期|087201-1-087201-12|共12页
  • 作者单位

    Harbin Institute of Technology, National Key Laboratory of Science and Technology on Tunable Laser, P.O. Box 3031, No. 2 YiKuang Street, Harbin, 150080, China;

    Harbin Institute of Technology, National Key Laboratory of Science and Technology on Tunable Laser, P.O. Box 3031, No. 2 YiKuang Street Harbin, 150080, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 英语
  • 中图分类 计量学;
  • 关键词

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