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Laser & Optoelectronics Progress, Volume. 56, Issue 10, 101007(2019)

Application of Non-Negative Matrix Factorization in Space Object Recognition

Jingjing Sun1,2、** and Fei Zhao1、*
Author Affiliations
  • 1 Key Laboratory of Computational Optical Imaging Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
  • 2 Univercity of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (0)
    References (21)
    Cited By (1)
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    Figures & Tables(12)
    Principle diagram of NMF

    Fig. 1. Principle diagram of NMF

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    Scanning mode

    Fig. 2. Scanning mode

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    Geometric model of laboratory

    Fig. 3. Geometric model of laboratory

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    Images of ten kinds of space objects

    Fig. 4. Images of ten kinds of space objects

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    Flow chart of experiment. (a) Feature extraction; (b) space object image recognition

    Fig. 5. Flow chart of experiment. (a) Feature extraction; (b) space object image recognition

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    Recognition rate of each NMF algorithm under number of samples is 100

    Fig. 6. Recognition rate of each NMF algorithm under number of samples is 100

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    Training time of each NMF algorithm under number of samples is 100

    Fig. 7. Training time of each NMF algorithm under number of samples is 100

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    Recognition rate of each NMF algorithm under different numbers of samples

    Fig. 8. Recognition rate of each NMF algorithm under different numbers of samples

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    Training time of each NMF algorithm under different numbers of samples

    Fig. 9. Training time of each NMF algorithm under different numbers of samples

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    • Table 1. Testing time of each NMF algorithm under number of samples is 100

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      Table 1. Testing time of each NMF algorithm under number of samples is 100

      AlgorithmEDNMFKLNMFSNMF2DED2DKL2DS(2D)2ED(2D)2KL(2D)2S
      Testing time /s0.9941.0141.0360.6620.6760.6650.7130.7340.722

    • Table 2. Testing time of each algorithm under optimal k (number of samples is 100)

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      Table 2. Testing time of each algorithm under optimal k (number of samples is 100)

      AlgorithmEDNMFKLNMFSNMF2DED2DKL2DS(2D)2ED(2D)2KL(2D)2S
      Testing time/s1.0311.0361.0140.5350.5480.5320.7220.7280.709

    • Table 3. Comparison between NMF and existing methods

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      Table 3. Comparison between NMF and existing methods

      MethodNMFMethod inRef. [2]Method inRef. [3]Method inRef. [4]
      Recognitionrate0.90.720.840.67
      Trainingtime /s38.91135.475748.65500.43
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    Jingjing Sun, Fei Zhao. Application of Non-Negative Matrix Factorization in Space Object Recognition[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101007

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    Paper Information

    Category: Image Processing

    Received: Nov. 27, 2018

    Accepted: Dec. 21, 2018

    Published Online: Jul. 4, 2019

    The Author Email: Sun Jingjing (sunjingjing16@mails.ucas.ac.cn), Zhao Fei (zhaofei@aoe.ac.cn)

    DOI:10.3788/LOP56.101007

    Topics

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