SAR image ATR using SVM with a low dimensional combined feature

Hongqiao Wang; Fuchun Sun; Zongtao Zhao; Yanning Cai

doi:10.1117/12.749742

15 November 2007 SAR image ATR using SVM with a low dimensional combined feature

Hongqiao Wang, Fuchun Sun, Zongtao Zhao, Yanning Cai

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67862J (2007) https://doi.org/10.1117/12.749742
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Hongqiao Wang, Fuchun Sun, Zongtao Zhao, and Yanning Cai "SAR image ATR using SVM with a low dimensional combined feature", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862J (15 November 2007); https://doi.org/10.1117/12.749742

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