A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

  • Zhenyu Yue
  • Fei GaoEmail author
  • Qingxu Xiong
  • Jun Wang
  • Teng Huang
  • Erfu Yang
  • Huiyu Zhou


Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural network (CNN)-based SAR ATR methods. However, the performance of CNN significantly deteriorates when the labeled samples are insufficient. To effectively utilize the unlabeled samples, we present a novel semi-supervised CNN method. In the training process of our method, the information contained in the unlabeled samples is integrated into the loss function of CNN. Specifically, we first utilize CNN to obtain the class probabilities of the unlabeled samples. Thresholding processing is performed to optimize the class probabilities so that the reliability of the unlabeled samples is improved. Afterward, the optimized class probabilities are used to calculate the scatter matrices of the linear discriminant analysis (LDA) method. Finally, the loss function of CNN is modified by the scatter matrices. We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of our method is significantly higher than other semi-supervised methods. It has been proved that our method can effectively improve the SAR ATR accuracy when labeled samples are insufficient.


Synthetic aperture radar Image recognition Convolutional neural network Semi-supervised learning Linear discriminant analysis 


Funding Information

This research was funded by the National Natural Science Foundation of China (No. 61771027, No. 61071139, No. 61471019, No. 61501011, and No. 61171122). E. Yang is supported in part under the RSE-NNSFC Joint Project (2017–2019) (No. 6161101383) with China University of Petroleum (Huadong). H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhenyu Yue
    • 1
  • Fei Gao
    • 1
    Email author
  • Qingxu Xiong
    • 1
  • Jun Wang
    • 1
  • Teng Huang
    • 1
  • Erfu Yang
    • 2
  • Huiyu Zhou
    • 3
  1. 1.School of Electronic Information EngineeringBeihang UniversityBeijingChina
  2. 2.Strathclyde Space Institute, Department of Design, Manufacture and Engineering ManagementUniversity of StrathclydeGlasgowUK
  3. 3.Department of InformaticsUniversity of LeicesterLeicesterUK

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