Volume 9 Issue 3
Jun.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(3): 525-538
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: QIN Xianxiang, YU Wangsheng, WANG Peng, et al. Weakly supervised classification of PolSAR images based on sample refinement with complex-valued convolutional neural network[J]. Journal of Radars, 2020, 9(3): 525–538. doi: 10.12000/JR20062
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Weakly Supervised Classification of PolSAR Images Based on Sample Refinement with Complex-Valued Convolutional Neural Network

DOI: 10.12000/JR20062
  • 1.

    Information and Navigation College, Air Force Engineering University, Xi’an 710077, China

  • 2.

    College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (41601436, 61403414, 61703423), The Natural Science Basic Research Plan in Shaanxi Province (2018JM4029)
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  • Corresponding author: QIN Xianxiang, qinxianxiang@126.com
  • Received Date: 2020-05-13
  • Rev Recd Date: 2020-06-26
    • Available Online: 2020-07-05
  • Publish Date: 2020-06-01
    Abstract

  • In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used.

     

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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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