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Volume 10 Issue 4
Aug.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(4): 516-530
Zhou Yu, Wang Hai-peng, Chen Si-zhe. SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching[J]. Journal of Radars, 2015, 4(6): 666-673. doi: 10.12000/JR15080
Citation: WANG Ruichuan and WANG Yanfei. Terrain classification of polarimetric SAR images using semi-supervised spatial-channel selective kernel network[J]. Journal of Radars, 2021, 10(4): 516–530. doi: 10.12000/JR21080
shu

Terrain Classification of Polarimetric SAR Images Using Semi-supervised Spatial-channel Selective Kernel Network

DOI: 10.12000/JR21080
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China

Funds:  The National Key Research and Development Program (2017YFB0503001)
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  • Corresponding author: WANG Yanfei, yfwang@mail.ie.ac.cn
  • Received Date: 2021-06-11
  • Rev Recd Date: 2021-06-22
    • Available Online: 2021-07-05
  • Publish Date: 2021-08-28
    Abstract
  • In this paper, a Spatial-Channel Selective Kernel Fully Convolutional Network (SCSKFCN) and a Semi-supervised Preselection-United Optimization (SPUO) method are proposed for polarimetric Synthetic Aperture Radar (SAR) image classification. Integrated with spatial-channel attention mechanism, SCSKFCN adaptively fuses features that have different sizes of reception field, and achieves promising classification performance. SPUO can efficiently extract information contained in unlabeled samples according to annotated samples. It utilizes K-Wishart distance to preselect unlabeled samples for pseudo label generation, and then optimizes SCSKFCN with both labeled and pseudo labeled samples. During the training process of SCSKFCN, a two-step verification mechanism is applied on pseudo labeled samples to reserve reliable samples for united optimization. The experimental results show that the proposed SCSKFCN-SPUO can achieve promising performance and efficiency using limited number of annotated pixels.

     

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    • References(35)
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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