Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410013(2021)

Synthetic Aperture Radar Image Target Recognition Based on Updated Classifier

Zhenzhong Zhang*
Author Affiliations
  • College of Equipment Management and Support, Engineering University of PAP, Xi’an, Shaanxi 710086, China
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    Figures & Tables(13)
    Structure of the CNN
    Flow chart of the SAR image target recognition based on updated classifier
    Target images in the MSTAR data set. (a) BMP2; (b) BTR70; (c) T72; (d) T62; (e) BRDM2; (f) BTR60; (g) ZSU23/4; (h) D7; (i) ZIL131; (j) 2S1
    Classification results under standard operating conditions
    Classification results of different methods under noise interference
    Comparison of results under small traning samples
    • Table 1. Test scenarios for standard operating conditions

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      Table 1. Test scenarios for standard operating conditions

      ClassTraining (17°)Test (15°)
      ConfigurationNumber of samplesConfigurationNumber of samples
      BMP2956323395639566c21195196196
      BTR70--233--196
      T72132232132812S7196195191
      T62--299--273
      BRDM2--298--274
      BTR60--256--195
      ZSU23/4--299--274
      D7--299--274
      ZIL131--299--274
      2S1--299--274
    • Table 2. Classification results of different methods under standard operating conditions unit: %

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      Table 2. Classification results of different methods under standard operating conditions unit: %

      MethodAverage recognition rate
      Ours99.08
      SRC96.42
      A-ConvNet98.53
      Aug-CNN98.91
      SVM+SRC97.48
    • Table 3. Classification results of our method under different decision reliability thresholds unit: %

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      Table 3. Classification results of our method under different decision reliability thresholds unit: %

      Threshold1.11.21.31.41.51.61.7
      Average recognition rate98.6298.8699.0299.0899.0398.7998.54
    • Table 4. Decision variable distribution of BMP2 test sample

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      Table 4. Decision variable distribution of BMP2 test sample

      Class12345678910
      Decision value 10.280.050.320.040.100.040.010.060.050.05
      Decision value 20.360.070.180.060.080.070.020.050.070.04
      Fused decision value0.320.060.250.050.050.060.020.060.060.05
    • Table 5. Average recognition rates of different test subsets unit: %

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      Table 5. Average recognition rates of different test subsets unit: %

      Test subsetPart 1Part 2Part 3Part 4All
      Average recognition rate69.3275.4585.6489.1279.86
    • Table 6. Test conditions for pitch angle difference

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      Table 6. Test conditions for pitch angle difference

      ClassTrainingTest
      DepressionNumber of samplesDepressionNumber of samples
      2S117°29930°45°288303
      BDRM217°29830°45°287303
      ZSU23/417°29930°45°288303
    • Table 7. Test results under different pitch angles unit: %

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      Table 7. Test results under different pitch angles unit: %

      MethodDepression
      30°45°
      Ours96.1272.74
      SRC92.1764.38
      A-ConvNet94.1265.93
      Aug-CNN95.3869.32
      SVM+SRC94.1666.08
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    Zhenzhong Zhang. Synthetic Aperture Radar Image Target Recognition Based on Updated Classifier[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410013

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    Paper Information

    Category: Image Processing

    Received: Sep. 23, 2020

    Accepted: Nov. 14, 2020

    Published Online: Jun. 30, 2021

    The Author Email: Zhenzhong Zhang (wjzhangzhenzhong@163.com)

    DOI:10.3788/LOP202158.1410013

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