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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    • References(22)
  • [1]
    盖旭刚, 陈晋汶, 韩俊, 等. 合成孔径雷达的现状与发展趋势[J]. 飞航导弹, 2011(3): 82–86, 95.

    GAI Xugang, CHEN Jinwen, HAN Jun, et al. Development status and trend of synthetic aperture radar[J]. Aerodynamic Missile Journal, 2011(3): 82–86, 95.
    [2]
    张红, 王超, 张波, 等. 高分辨率SAR图像目标识别[M]. 北京: 科学出版社, 2009.

    ZHANG Hong, WANG Chao, ZHANG Bo, et al. Target Recognition in High Resolution SAR Images[M]. Beijing: Science Press, 2009.
    [3]
    MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
    [4]
    王瑞霞, 林伟, 毛军. 基于小波变换和PCA的SAR图像相干斑抑制[J]. 计算机工程, 2008, 34(20): 235–237. doi: 10.3969/j.issn.1000-3428.2008.20.086

    WANG Ruixia, LIN Wei, and MAO Jun. Speckle suppression for SAR image based on wavelet transform and PCA[J]. Computer Engineering, 2008, 34(20): 235–237. doi: 10.3969/j.issn.1000-3428.2008.20.086
    [5]
    CHEN Sizhe and WANG Haipeng. SAR target recognition based on deep learning[C]. 2014 International Conference on Data Science and Advanced Analytics, Shanghai, China, 2015.
    [6]
    田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. doi: 10.12000/JR16037

    TIAN Zhuangzhuang, ZHAN Ronghui, HU Jiemin, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325. doi: 10.12000/JR16037
    [7]
    CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720
    [8]
    FURUKAWA H. Deep learning for target classification from SAR imagery: Data augmentation and translation invariance[R]. SANE2017-30, 2017.
    [9]
    袁媛, 袁昊, 雷玲, 等. 一种同步轨道星机双基SAR成像方法[J]. 雷达科学与技术, 2007, 5(2): 128–132. doi: 10.3969/j.issn.1672-2337.2007.02.011

    YUAN Yuan, YUAN Hao, LEI Ling, et al. An imaging method of GEO Spaceborne-Airborne Bistatic SAR[J]. Radar Science and Technology, 2007, 5(2): 128–132. doi: 10.3969/j.issn.1672-2337.2007.02.011
    [10]
    史洪印, 周荫清, 陈杰. 同步轨道星机双基地三通道SAR地面运动目标指示算法[J]. 电子与信息学报, 2009, 31(8): 1881–1885.

    SHI Hongyin, ZHOU Yinqing, and CHEN Jie. An algorithm of GEO spaceborne-airborne bistatic three-channel SAR ground moving target indication[J]. Journal of Electronics &Information Technology, 2009, 31(8): 1881–1885.
    [11]
    LI Zhuo, LI Chunsheng, YU Ze, et al. Back projection algorithm for high resolution GEO-SAR image formation[C]. 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 2011: 336–339.
    [12]
    ZHANG Fan, HU Chen, YIN Qiang, et al. Multi-aspect-aware bidirectional LSTM networks for synthetic aperture radar target recognition[J]. IEEE Access, 2017, 5: 26880–26891. doi: 10.1109/ACCESS.2017.2773363
    [13]
    PEI Jifang, HUANG Yulin, HUO Weibo, et al. SAR automatic target recognition based on Multiview deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2196–2210. doi: 10.1109/TGRS.2017.2776357
    [14]
    邹浩, 林赟, 洪文. 采用深度学习的多方位角SAR图像目标识别研究[J]. 信号处理, 2018, 34(5): 513–522. doi: 10.16798/j.issn.1003-0530.2018.05.002

    ZOU Hao, LIN Yun, and HONG Wen. Research on multi-aspect SAR images target recognition using deep learning[J]. Journal of Signal Processing, 2018, 34(5): 513–522. doi: 10.16798/j.issn.1003-0530.2018.05.002
    [15]
    ZHAO Pengfei, LIU Kai, ZOU Hao, et al. Multi-stream convolutional neural network for SAR automatic target recognition[J]. Remote Sensing, 2018, 10(9): 1473. doi: 10.3390/rs10091473
    [16]
    ZHANG Fan, FU Zhenzhen, ZHOU Yongsheng, et al. Multi-aspect SAR target recognition based on space-fixed and space-varying scattering feature joint learning[J]. Remote Sensing Letters, 2019, 10(10): 998–1007. doi: 10.1080/2150704X.2019.1635287
    [17]
    TAN Mingxing and LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks[J]. arXiv: 1905.11946, 2019.
    [18]
    CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv: 1406.1078, 2014.
    [19]
    CAI Jie, MENG Zibo, KHAN A S, et al. Island loss for learning discriminative features in facial expression recognition[C]. The 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, China, 2018: 302–309.
    [20]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
    [21]
    HOCHREITER S and SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735–1780. doi: 10.1162/neco.1997.9.8.1735
    [22]
    WEN Yandong, ZHANG Kaipeng, LI Zhifeng, et al. A discriminative feature learning approach for deep face recognition[C]. The 14th European Conference on Computer Vision – ECCV 2016, Amsterdam, The Netherlands, 2016.
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