Volume 13 Issue 5
Sep.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(5): 985-1003
NIE Lin, WEI Shunjun, LI Jiahui, et al. Active blanket jamming suppression method for spaceborne SAR images based on regional feature refinement perceptual learning[J]. Journal of Radars, 2024, 13(5): 985–1003. doi: 10.12000/JR24072
Citation: NIE Lin, WEI Shunjun, LI Jiahui, et al. Active blanket jamming suppression method for spaceborne SAR images based on regional feature refinement perceptual learning[J]. Journal of Radars, 2024, 13(5): 985–1003. doi: 10.12000/JR24072
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Active Blanket Jamming Suppression Method for Spaceborne SAR Images Based on Regional Feature Refinement Perceptual Learning

DOI: 10.12000/JR24072 CSTR: 32380.14.JR24072
  • 1.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    Beijing Institute of Control and Electronics Technology, Beijing 100038, China

Funds:  The National Natural Science Foundation of China (62271108)
More Information
  • Corresponding author: WEI Shunjun, weishunjun@uestc.edu.cn
  • Received Date: 2024-04-25
  • Rev Recd Date: 2024-06-07
    • Available Online: 2024-06-16
  • Publish Date: 2024-07-03
    Abstract
  • Spaceborne Synthetic Aperture Radar (SAR) systems are often subject to strong electromagnetic interference, resulting in imaging quality degradation. However, existing image domain-based interference suppression methods are prone to image distortion and loss of texture detail information, among other difficulties. To address these problems, this paper proposes a method for suppressing active suppression interferences inspaceborne SAR images based on perceptual learning of regional feature refinement. First, an active suppression interference signal and image model is established in the spaceborne SAR image domain. Second, a high-precision interference recognition network based on regional feature perception is designed to extract the active suppression interference pattern features of the involved SAR image using an efficient channel attention mechanism, consequently resulting in effective recognition of the interference region of the SAR image. Third, a multivariate regional feature refinement interference suppression network is constructed based on the joint learning of the SAR image and suppression interference features, which are combined to form the SAR image and suppression interference pattern. A feature refinement interference suppression network is then constructed based on the joint learning of the SAR image and suppression interference feature. The network slices the SAR image into multivariate regions, and adopts multi-module collaborative processing of suppression interference features on the multivariate regions to realize refined suppression of the active suppression interference of the SAR image under complex conditions. Finally, a simulation dataset of SAR image active suppression interference is constructed, and the evaluated Sentinel-1 data are used for experimental verification and analysis. The experimental results show that the proposed method can effectively recognize and suppress various typical active suppression interferences in spaceborne SAR images.

     

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