Volume 11 Issue 5
Oct.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(5): 884-896
DU Lan, WANG Zilin, GUO Yuchen, et al. Adaptive region proposal selection for SAR target detection using reinforcement learning[J]. Journal of Radars, 2022, 11(5): 884–896. doi: 10.12000/JR22121
Citation: DU Lan, WANG Zilin, GUO Yuchen, et al. Adaptive region proposal selection for SAR target detection using reinforcement learning[J]. Journal of Radars, 2022, 11(5): 884–896. doi: 10.12000/JR22121
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Adaptive Region Proposal Selection for SAR Target Detection Using Reinforcement Learning

doi: 10.12000/JR22121
  • 1.

    National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 2.

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (U21B2039)
More Information
  • Corresponding author: DU Lan, dulan@mail.xidian.edu.cn
  • Received Date: 2022-06-22
  • Accepted Date: 2022-08-25
  • Rev Recd Date: 2022-08-24
    • Available Online: 2022-08-26
  • Publish Date: 2022-09-02
    Abstract
  • Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance.

     

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