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Optical Engineering

Target recognition for small samples of ladar range image using classifier ensembles
Author(s): Zheng-Jun Liu; Qi Li; Zhi-Wei Xia; Qi Wang
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Paper Abstract

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.

Paper Details

Date Published: 6 August 2012
PDF: 13 pages
Opt. Eng. 51(8) 087201 doi: 10.1117/1.OE.51.8.087201
Published in: Optical Engineering Volume 51, Issue 8
Show Author Affiliations
Zheng-Jun Liu, Harbin Institute of Technology (China)
Qi Li, Harbin Institute of Technology (China)
Zhi-Wei Xia, Harbin Institute of Technology (China)
Qi Wang, Harbin Institute of Technology (China)

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