Electronics Optics & Control, Volume. 28, Issue 6, 29(2021)

An SAR Target Classification Method Based on BM3D Denoising and Extreme Learning Machine

LIU Zhichao1,2 and QU Baida1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(10)

    [1] [1] EL-DARYMLI K,GILL E W,MCGUIRE P,et al.Automatic target recognition in synthetic aperture radar imagery:a state-of-the-art review [J].IEEE Access,2016,4:6014-6058.

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    [8] [8] DONG G G,KUANG G Y,WANG N,et al.SAR target re-cognition via joint sparse representation of monogenic signal [J].IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2015,8(7):1-13.

    [10] [10] DING B Y,WEN G J,ZHONG J R,et al.A robust similarity measure for attributed scattering center sets with application to SAR ATR[J].Neurocomputing,2017,219:130-143.

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    [13] [13] THIAGARAIANM J,RAMAMURTHY K,KNEE P,et al.Sparse representations for automatic target classification in SAR images[C]//The 4th International Symposium on Communications,Control and Signal Processing(ISCCSP), 2010: 1-4.

    [14] [14] CHEN S Z,WANG H P,XU F,et al.Target classification using the deep convolutional networks for SAR images [J].IEEE Transactions on Geoscience and Remote Sensing,2016,47(6):1685-1697.

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    LIU Zhichao, QU Baida. An SAR Target Classification Method Based on BM3D Denoising and Extreme Learning Machine[J]. Electronics Optics & Control, 2021, 28(6): 29

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    Paper Information

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    Received: Jun. 2, 2020

    Accepted: --

    Published Online: Jul. 16, 2021

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2021.06.007

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