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Proceedings Paper

SAR target feature extraction and recognition based multilinear principal component analysis
Author(s): Liping Hu; Xiaoyu Xing
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Paper Abstract

In this paper, a multilinear principal component analysis (MPCA) algorithm is applied to dimensionality reduction in synthetic aperture radar (SAR) images target feature extraction. Firstly, the MPCA algorithm is used to find the projection matrices in each mode and perform dimensionality reduction in all tensor modes. And then the distances of the feature tensors of the testing and training are computed for classification. Experimental results based on the moving and stationary target recognition (MSTAR) data indicate that compared with the existing methods, such as principal component analysis (PCA), 2-dimensional PCA (2DPCA), and generalized low rank approximations of matrices (GLRAM), the MPCA algorithm achieves the best recognition performance with acceptable feature dimensionality.

Paper Details

Date Published: 24 November 2014
PDF: 6 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93010L (24 November 2014); doi: 10.1117/12.2069953
Show Author Affiliations
Liping Hu, Science and Technology on Electromagnetic Scattering Lab. (China)
Xiaoyu Xing, Science and Technology on Electromagnetic Scattering Lab. (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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