Adaptive multiparameter spectral feature analysis for synthetic aperture radar target recognition

Xiangrong Zhang; Licheng Jiao; Sisi Zhou; Nan Zhou; Jie Feng

doi:10.1117/1.OE.51.8.087203

16 August 2012 Adaptive multiparameter spectral feature analysis for synthetic aperture radar target recognition

Xiangrong Zhang, Licheng Jiao, Sisi Zhou, Nan Zhou, Jie Feng

Author Affiliations +

Optical Engineering, Vol. 51, Issue 8, 087203 (August 2012). https://doi.org/10.1117/1.OE.51.8.087203

Abstract

A feature extraction algorithm based on spectral clustering with adaptive multiparameters is proposed for synthetic aperture radar automatic target recognition (SAR-ATR). Spectral clustering has been widely applied in computer vision for its good performance. Meanwhile, the spectral mapping step in it has the property of feature space transformation. Spectral clustering based target feature extraction for SAR-ATR is constructed according to the framework of out-of-sample extensions in weighted kernel principal component analysis. To avoid the scaling parameter selection in spectral feature analysis (SFA) and eliminate the influence of scaling parameter on feature extraction performance as well, the multiple scaling parameters are calculated adaptively by local neighborhoods. Because the local statistics of the neighborhood of each point are taken into consideration, its performance is better than using only one fixed parameter. Based on the extracted features, target recognition is performed by the support vector machine for its good generalization capability. The experimental results show that the multiparameter SFA outperforms the principal component analysis, kernel principal component analysis and SFA with the selected scaling parameter for SAR target recognition in terms of recognition accuracy.

Citation Download Citation

Xiangrong Zhang, Licheng Jiao, Sisi Zhou, Nan Zhou, and Jie Feng "Adaptive multiparameter spectral feature analysis for synthetic aperture radar target recognition," Optical Engineering 51(8), 087203 (16 August 2012). https://doi.org/10.1117/1.OE.51.8.087203

Published: 16 August 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available