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Acta Optica Sinica, Volume. 39, Issue 3, 0311002(2019)

Infrared Target Simulation Method Based on Generative Adversarial Neural Networks

Jiangrong Xie1,2, Fanming Li1,3、*, Hong Wei1, and Bing Li1
Author Affiliations
  • 1 Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 3 Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    Cited By (9)
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    Figures & Tables(10)
    Framework of CGAN model

    Fig. 1. Framework of CGAN model

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    Structural diagram of generation network of DCGAN

    Fig. 2. Structural diagram of generation network of DCGAN

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    Flow chart of simulation algorithm based on C-DCGAN model

    Fig. 3. Flow chart of simulation algorithm based on C-DCGAN model

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    Structural diagram of generation network of C-DCGAN

    Fig. 4. Structural diagram of generation network of C-DCGAN

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    Structural diagram of discrimination network of C-DCGAN

    Fig. 5. Structural diagram of discrimination network of C-DCGAN

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    Trends of loss function on infrared dataset. (a) Ld_loss_real; (b) Ld_loss_fake; (c) Ld_loss; (d) Lg_loss

    Fig. 6. Trends of loss function on infrared dataset. (a) Ld_loss_real; (b) Ld_loss_fake; (c) Ld_loss; (d) Lg_loss

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    Effect image of infrared targets generated by C-DCGAN

    Fig. 7. Effect image of infrared targets generated by C-DCGAN

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    Effect image of MNIST samples generated by C-DCGAN

    Fig. 8. Effect image of MNIST samples generated by C-DCGAN

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    • Table 1. Accuracy comparison among different classification methods%

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      Table 1. Accuracy comparison among different classification methods%

      MethodMNISTIR dataset
      Linear classifier (1-layer NN)91.6080.16
      K-nearest-neighbors, Euclidean (L2)95.0081.44
      C-DCGAN+L2 SVM93.6984.31

    • Table 2. Performance comparison among different data augmentation methods%

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      Table 2. Performance comparison among different data augmentation methods%

      MethodMNISTIR dataset
      Copy95.4082.22
      Affine transformation96.8384.17
      C-DCGAN generator96.1585.61
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    Jiangrong Xie, Fanming Li, Hong Wei, Bing Li. Infrared Target Simulation Method Based on Generative Adversarial Neural Networks[J]. Acta Optica Sinica, 2019, 39(3): 0311002

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    Paper Information

    Category: Imaging Systems

    Received: Sep. 26, 2018

    Accepted: Nov. 8, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0311002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology