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Proceedings Paper

SAR image ATR using SVM with a low dimensional combined feature
Author(s): Hongqiao Wang; Fuchun Sun; Zongtao Zhao; Yanning Cai
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Paper Abstract

In this paper, a grayscale wavelet moment and entropy combined feature, which can well represent the images with much lower dimensions, is proposed for the MSTAR targets' classification, also a discriminative feature selection and evaluation method for multi-class targets is presented. By introducing SVM as the classifier to some simulation tests, it can be shown that the grayscale wavelet moment and entropy combined feature has good capability for translation, scaling and rotation transformation in both local information and global information conditions, by the reasons of wavelet moments' multi-resolution analysis, moment invariant quality and entropy's statistic quality for image disorder. The test also confirms that this feature has a significant improvement on classificatory accuracy using SVM with a lower feature dimension than the other features such as the single wavelet moment, Hu's moment and PCA.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862J (15 November 2007); doi: 10.1117/12.749742
Show Author Affiliations
Hongqiao Wang, Tsinghua Univ. (China)
Xi’an Research Institute of High Technology (China)
Fuchun Sun, Tsinghua Univ. (China)
Zongtao Zhao, Xi’an Research Institute of High Technology (China)
Yanning Cai, Xi’an Research Institute of High Technology (China)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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