Volume 11 Issue 1
Feb.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(1): 168-182
LYU Xiaoling, QIU Xiaolan, YU Wenming, et al. Simulation-assisted SAR target classification based on unsuperviseddomain adaptation and model interpretability analysis[J]. Journal of Radars, 2022, 11(1): 168–182. doi: 10.12000/JR21179
Citation: LYU Xiaoling, QIU Xiaolan, YU Wenming, et al. Simulation-assisted SAR target classification based on unsupervised domain adaptation and model interpretability analysis[J]. Journal of Radars, 2022, 11(1): 168–182. doi: 10.12000/JR21179
shu

Simulation-assisted SAR Target Classification Based on Unsupervised Domain Adaptation and Model Interpretability Analysis

doi: 10.12000/JR21179
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    Key Laboratory of Technology in Geospatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 4.

    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China

  • 5.

    Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China

Funds:  The National Natural Science Foundation of China (61991421)
More Information
  • Corresponding author: QIU Xiaolan, xlqiu@mail.ie.ac.cn
  • Received Date: 2021-11-14
  • Accepted Date: 2022-01-14
  • Rev Recd Date: 2022-01-13
    • Available Online: 2022-01-20
  • Publish Date: 2022-02-16
    Abstract
  • Convolutional Neural Networks (CNNs) are widely used in optical image classification. In the case of Synthetic Aperture Radar (SAR) images, obtaining sufficient training examples for CNNs is challenging due to the difficulties in and high cost of data annotation. Meanwhile, with the advancement of SAR image simulation technology, generating a large number of simulated SAR images with annotation is not difficult. However, due to the inevitable difference between simulated and real SAR images, it is frequently difficult to directly support the real SAR image classification. As a result, this study proposes a simulation-assisted SAR target classification method based on unsupervised domain adaptation. The proposed method integrates Multi-Kernel Maximum Mean Distance (MK-MMD) with domain adversarial training to address the domain shift problem encountered during task transition from simulated to real-world SAR image classification. Furthermore, Layer-wise Relevance Propagation (LRP) and Contrastive Layer-wise Relevance Propagation (CLRP) are utilized to explore how the proposed method influences the model decision. The experimental results show that by modifying the focus areas of the model to obtain domain-invariant features for classification, the proposed method can significantly improve classification accuracy.

     

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