Title :
The Performance Comparison of Adaboost and SVM Applied to SAR ATR
Author :
Wang, Ying ; Han, Ping ; Xiaoguang Lu ; Wu, Renbiao ; Huang, Jingxiong
Author_Institution :
Tianjin Key Lab for Adv. Signal Process., Civil Aviation Univ. of China, Tianjin
Abstract :
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
Keywords :
feature extraction; image classification; principal component analysis; radar computing; radar imaging; radar target recognition; support vector machines; synthetic aperture radar; Adaboost classifier; MSTAR data sets; PCA; SAR ATR; SVM classifier; automatic target recognition; principal component analysis; synthetic aperture radar; Eigenvalues and eigenfunctions; Feature extraction; Information processing; Matrix decomposition; Principal component analysis; Radar signal processing; Support vector machine classification; Support vector machines; Synthetic aperture radar; Target recognition; Adaboost classifier; PCA; SARATR; SVM;
Conference_Titel :
Radar, 2006. CIE '06. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
0-7803-9582-4
Electronic_ISBN :
0-7803-9583-2
DOI :
10.1109/ICR.2006.343515
