Laser & Optoelectronics Progress, Volume. 55, Issue 11, 111504(2018)

Aurora Sequence Classification Based on Deep Learning

Hao Zhang** and Changhong Chen*
Author Affiliations
  • College of Communication and Information Technology, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China
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    Figures & Tables(8)
    Framework of our method
    Four categories of sample images at 557.7 nm
    CNN with attribute constraints
    Feature maps of facula attributes learned from our network. (a) Arc image; (b) hot-spot image; (c) arc feature map; (d) hot-spot feature map
    Classification accuracy of aurora images with different ω
    Comparison of classification accuracy on aurora sequences
    Distribution of four kinds of aurora
    • Table 1. Comparison of classification accuracy on aurora images

      View table

      Table 1. Comparison of classification accuracy on aurora images

      MethodAllArcDraperyRadialHot-spot
      Original CNN0.950.980.890.970.90
      CNN-LSTM on parallel connectionOur method (ω=4)0.960.950.980.980.910.890.910.900.980.95
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    Hao Zhang, Changhong Chen. Aurora Sequence Classification Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111504

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    Paper Information

    Category: Machine Vision

    Received: Apr. 15, 2018

    Accepted: May. 29, 2018

    Published Online: Aug. 14, 2019

    The Author Email: Hao Zhang (ztqup666@outlook.com), Changhong Chen (chenchh@njupt.edu.cn)

    DOI:10.3788/LOP55.111504

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