A novel separability objective function in CNN for feature extraction of SAR images

Fei Gao, Meng Wang, Jun Wang, Erfu Yang, Huiyu Zhou

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Convolutional neural network (CNN) has become a promising method for Synthetic aperture radar (SAR) target recognition. Existing CNN models aim at seeking the best separation between classes, but rarely care about the separability of them. We performs a separability measure by analyzing the property of linear separability, and proposes an objective function for CNN to extract linearly separable features. The experimental results indicate the output features are linearly separable, and the classification results are comparable with the other state of the art techniques.

Original languageEnglish
Pages (from-to)423-429
Number of pages7
JournalChinese Journal of Electronics
Issue number2
Publication statusPublished - 1 Mar 2019


  • classification
  • convolution neural network (CNN)
  • linear separability
  • objective function
  • synthetic aperture radar (SAR)

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