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.
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