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Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings

Research Article

Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition

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  • @INPROCEEDINGS{10.1007/978-3-030-69066-3_35,
        author={Wei Qu and Gang Yao and Weigang Zhu},
        title={Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition},
        proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings},
        proceedings_a={AICON},
        year={2021},
        month={7},
        keywords={Deep convolutional neural network SAR Target recognition},
        doi={10.1007/978-3-030-69066-3_35}
    }
    
  • Wei Qu
    Gang Yao
    Weigang Zhu
    Year: 2021
    Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition
    AICON
    Springer
    DOI: 10.1007/978-3-030-69066-3_35
Wei Qu1, Gang Yao2, Weigang Zhu1
  • 1: Space Engineering University
  • 2: Beijing Institute of Tracking and Telecommunication Technology

Abstract

As a frontier hot spot in the current image processing field, deep learning has unparalleled superiority in feature extraction. Deep learning uses deep network structure to perform layer-by-layer nonlinear transformation, which can achieve the approximation of complex functions. From low-level to high-level, the representation of features becomes more and more abstract, and the more essential the original data is described. Aiming at the problem of SAR image target detection, this paper studies the influence of the number of convolution kernels, the size of the convolution kernel and the number of convolution layers in the deep convolutional neural network on SAR target recognition.

Keywords
Deep convolutional neural network SAR Target recognition
Published
2021-07-16
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-69066-3_35
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