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Volume 6 Issue 5
Oct.  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(5): 492-502
Hong Wen. Hybrid-polarity Architecture Based Polarimetric SAR: Principles and Applications (in English)[J]. Journal of Radars, 2016, 5(6): 559-595. doi: 10.12000/JR16074
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
shu

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

DOI: 10.12000/JR17078

  • College of Communication Engineering, Chongqing University, Chongqing 400044, China

Funds:  The National Natural Science Foundation of China (61301224)
  • Received Date: 2017-08-18
  • Rev Recd Date: 2017-10-22
    • Available Online: 2017-10-27
  • Publish Date: 2017-10-28
    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(25)
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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