Volume 7 Issue 5
Nov.  2018
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Article Navigation >  Journal of Radars  > 2018  >  7(5): 622-631
Wu Yong, Wang Jun. Application of Mixed Kalman Filter to Passive Radar Target Tracking[J]. Journal of Radars, 2014, 3(6): 652-659. doi: 10.12000/JR14113
Citation: Yu Lingjuan, Wang Yadong, Xie Xiaochun, Lin Yun, Hong Wen. SAR ATR Based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622-631. doi: 10.12000/JR18066
shu

SAR ATR Based on FCNN and ICAE

DOI: 10.12000/JR18066
  • ①.

    School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

  • ②.

    Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

  • ③.

    School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China

Funds:  The National Natural Science Foundation of China (61431018, 61501210, 61571421), The Natural Science Foundation of Jiangxi Province (20161BAB202054), The Science and Technology Project of Jiangxi Provincial Education Department (GJJ150684, GJJ170825)
  • Received Date: 2018-08-31
  • Rev Recd Date: 2018-10-20
  • Publish Date: 2018-10-28
    Abstract
  • In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded.

     

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    • References(24)
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