Volume 10 Issue 6
Dec.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(6): 895-904
ZHAO Pengfei and HUANG Lijia. Target recognition method for multi-aspect synthetic aperture radar images based on EfficientNet and BiGRU[J]. Journal of Radars, 2021, 10(6): 895–904. doi: 10.12000/JR20133
Citation: ZHAO Pengfei and HUANG Lijia. Target recognition method for multi-aspect synthetic aperture radar images based on EfficientNet and BiGRU[J]. Journal of Radars, 2021, 10(6): 895–904. doi: 10.12000/JR20133
shu

Target Recognition Method for Multi-aspect Synthetic Aperture Radar Images Based on EfficientNet and BiGRU

DOI: 10.12000/JR20133
  • 1.

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

  • 2.

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

  • 3.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The National Natural Science Foundation of China (61991420, 62022082), Special Support of Youth Innovation Promotion Association Chinese Academy of Sciences
More Information
  • Corresponding author: HUANG Lijia, iecas8huanglijia@163.com
  • Received Date: 2020-10-26
  • Rev Recd Date: 2020-12-21
    • Available Online: 2021-01-07
  • Publish Date: 2021-01-07
    Abstract
  • Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) has been extensively applied in military and civilian fields. However, SAR images are very sensitive to the azimuth of the images, as the same target can differ greatly from different aspects. This means that more reliable and robust multiaspect ATR recognition is required. In this paper, we propose a multiaspect ATR model based on EfficientNet and BiGRU. To train this model, we use island loss, which is more suitable for SAR ATR. Experimental results have revealed that our proposed method can achieve 100% accuracy for 10-class recognition on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The SAR targets in three special imaging cases with large depression angles, version variants, and configuration variants reached recognition accuracies of 99.68%, 99.95%, and 99.91%, respectively. In addition, the proposed method achieves satisfactory accuracy even with smaller datasets. Our experimental results show that our proposed method outperforms other state-of-the-art ATR methods on most MSTAR datasets and exhibits a certain degree of robustness.

     

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