Volume 12 Issue 2
Apr.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(2): 441-455
TIAN Ye, DING Chibiao, ZHANG Fubo, et al. SAR building area layover detection based on deep learning[J]. Journal of Radars, 2023, 12(2): 441–455. doi: 10.12000/JR23033
Citation: TIAN Ye, DING Chibiao, ZHANG Fubo, et al. SAR building area layover detection based on deep learning[J]. Journal of Radars, 2023, 12(2): 441–455. doi: 10.12000/JR23033
shu

SAR Building Area Layover Detection Based on Deep Learning

DOI: 10.12000/JR23033
  • 1.

    National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

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

  • 3.

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

Funds:  National Key R&D Program of China (2021YFA0715404)
More Information
  • Corresponding author: ZHANG Fubo, zhangfb@aircas.ac.cn
  • Received Date: 2023-03-11
  • Rev Recd Date: 2023-04-02
    • Available Online: 2023-04-06
  • Publish Date: 2023-04-24
    Abstract
  • Building layover detection is a crucial step in the 3D Synthetic Aperture Radar (SAR) imaging process in urban areas. It affects imaging efficiency and directly influences the final image quality. Currently, algorithms used for layover detection struggle to extract long-range global spatial characteristics and fail to fully exploit the rich features of layover in multi-channel SAR data. To address the issue of insufficient accuracy in existing layover detection algorithms to meet the requirements of urban 3D SAR imaging, this paper proposes a deep learning-powered SAR urban layover detection method that combines the advantages of the Vision Transformer (ViT) model and Convolutional Neural Network (CNN). The ViT model can efficiently extract global and long-range features through a self-attention mechanism, whereas the CNN has strong local feature extraction capabilities. Furthermore, the proposed method in this paper incorporates a module for investigating inter-channel layover features and interferometric phase layover features based on expert knowledge, which improves the accuracy and robustness of the algorithm while effectively decreasing the training pressure on the model in small-sample datasets. Finally, the proposed algorithm is tested on a self-built airborne array SAR dataset, and experimental findings revealed that the proposed algorithm achieves a detection accuracy of >94%, which is significantly higher than other layover detection algorithms, completely revealing the effectiveness of this method.

     

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