A method of aircraft image target recognition based on modified PCA features and SVM

Author

Wang, Donghe ; He, Xin ; Zhonghui, Wei ; Yu, Huilong

Author_Institution

Changchun Inst. of Opt. Fine Mech. & Phys., Chinese Acad. of Sci., Changchun, China

fYear

2009

fDate

16-19 Aug. 2009

Abstract

Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: dimensionality reduction and classifier. After pretreatment on original features a self-organizing neural network trained with the Hebbian rule is used to extract the principal component features. Then a classifier based on directed acyclic graph support vector machines (DAGSVM) is adopted to recognize more than two types of aircraft targets. The experiment results show the proposed method achieves better subset features and higher recognition rate.

Keywords

Hebbian learning; aircraft; feature extraction; image classification; neural nets; principal component analysis; support vector machines; ATR; Hebbian rule; SVM; aircraft image target recognition; classifier; dimensionality reduction; directed acyclic graph support vector machines; feature extraction; modified PCA; principal component analysis; self-organizing neural network; Aerospace electronics; Aircraft; Algorithms; Data mining; Feature extraction; Instruments; Neural networks; Principal component analysis; Support vector machines; Target recognition; PCA; SVM; classifier design; dimensionality reduction; feature extraction; feature selection; target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-3863-1

Electronic_ISBN

978-1-4244-3864-8

Type

conf

DOI

10.1109/ICEMI.2009.5274100

Filename

5274100