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The Performance Comparison of Adaboost and SVM Applied to SAR ATR

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5 Author(s)
Ying Wang ; Tianjin Key Lab for Adv. Signal Process., Civil Aviation Univ. of China, Tianjin ; Ping Han ; Xiaoguang Lu ; Renbiao Wu
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In this paper, Adaboost and SVM are applied to SAR ATR (synthetic aperture radar automatic target recognition) respectively. The performance of these two classifiers is analyzed and compared in target aspect window with different size. First, PCA (principal component analysis) features are selected as target feature, and then Adaboost.Ml and SVM are used to classify, respectively. Experimental results based on MSTAR data sets show that Adaboost classifier has better robustness than SVM classifier

Published in:

Radar, 2006. CIE '06. International Conference on

Date of Conference:

16-19 Oct. 2006