Title :
Two-dimensional PCA for SAR automatic target recognition
Author :
Lu, XiaoGuang ; Han, Ping ; Wu, Renbiao
Author_Institution :
Civil Aviation Univ. of China, Tianjin
Abstract :
In this paper, a new technique for synthetic aperture radar (SAR) automatic target recognition (ATR) is developed, which is builded upon two-dimensional principle component analysis (2DPCA). First, 2DPCA is applied to extract features in frequency domain, which is based on image matrix directly. Then support vector machine (SVM) is used for classification. Experimental results on MSTAR dataset show that the 2DPCA method both gives higher recognition rate, and are computationally more efficient than PCA.
Keywords :
feature extraction; image classification; principal component analysis; radar imaging; radar target recognition; support vector machines; synthetic aperture radar; 2DPCA; MSTAR dataset; SAR automatic target recognition; feature extraction; frequency domain; image classification; image matrix; support vector machine; synthetic aperture radar; Covariance matrix; Eigenvalues and eigenfunctions; Feature extraction; Image analysis; Principal component analysis; Radar signal processing; Support vector machine classification; Support vector machines; Synthetic aperture radar; Target recognition;
Conference_Titel :
Synthetic Aperture Radar, 2007. APSAR 2007. 1st Asian and Pacific Conference on
Conference_Location :
Huangshan
Print_ISBN :
978-1-4244-1188-7
Electronic_ISBN :
978-1-4244-1188-7
DOI :
10.1109/APSAR.2007.4418662
