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| Citation: | Zhang Xinzheng, Tan Zhiying, Wang Yijian. SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion[J]. Journal of Radars, 2017, 6(5): 492-502. doi: 10.12000/JR17078
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SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion
DOI: 10.12000/JR17078
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Abstract
In this paper, we present a Synthetic Aperture Radar (SAR) image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM) features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database,and they demonstrate the effectiveness of the proposed approach. -
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References
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- Figure 1. Multiple 2D slices of three targets
- Figure 2. Comparisons of 2D-slice Zernike moments of three targets
- Figure 3. The procedure of based on fusion of multi-feature multiple representation classifier
- Figure 4. Optical images of three targets
- Figure 5. Microwave images of three targets
- Figure 6. Recognition rate of three algorithms with feature dimension
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Figure 7. Recognition rates of three algorithms with regularized parameters
$ λ $ -
Figure 8. Recognition rate of SCBF with the regularization parameters
$ λ_1 $ $ λ_2 $ - Figure 9. Histogram of recognition rate of three algorithms at large pitch angle