SAR target feature extraction and recognition based multilinear principal component analysis

Liping Hu; Xiaoyu Xing

doi:10.1117/12.2069953

24 November 2014 SAR target feature extraction and recognition based multilinear principal component analysis

Liping Hu, Xiaoyu Xing

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93010L (2014) https://doi.org/10.1117/12.2069953
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

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.

Citation Download Citation

Liping Hu and Xiaoyu Xing "SAR target feature extraction and recognition based multilinear principal component analysis", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93010L (24 November 2014); https://doi.org/10.1117/12.2069953

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