Volume 10 Issue 1
Feb.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(1): 159-172
MA Lin, PAN Zongxu, HUANG Zhongling, et al. Multichannel false-target discrimination in SAR images based on sub-aperture and full-aperture feature learning[J]. Journal of Radars, 2021, 10(1): 159–172. doi: 10.12000/JR20106
Citation: MA Lin, PAN Zongxu, HUANG Zhongling, et al. Multichannel false-target discrimination in SAR images based on sub-aperture and full-aperture feature learning[J]. Journal of Radars, 2021, 10(1): 159–172. doi: 10.12000/JR20106
shu

Multichannel False-target Discrimination in SAR Images Based on Sub-aperture and Full-aperture Feature Learning

doi: 10.12000/JR20106
  • 1.

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

  • 2.

    Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China

  • 3.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The National Natural Science Foundation of China (61701478)
More Information
  • Corresponding author: PAN Zongxu, zxpan@mail.ie.ac.cn
  • Received Date: 2020-07-23
  • Rev Recd Date: 2020-09-09
    • Available Online: 2020-10-09
  • Publish Date: 2021-02-25
    Abstract
  • False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.

     

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