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

fYear

2006

fDate

16-19 Oct. 2006

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar, 2006. CIE '06. International Conference on

Conference_Location

Shanghai

Print_ISBN

0-7803-9582-4

Electronic_ISBN

0-7803-9583-2

Type

conf

DOI

10.1109/ICR.2006.343515

Filename

4148492