• Laser & Optoelectronics Progress
  • Vol. 57, Issue 4, 041004 (2020)
Xiaowen Liu1, Juncheng Lei1, and Yanpeng Wu2、*
Author Affiliations
  • 1Departanment Information Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
  • 2Department of Information Science and Engineering, Hunan First Normal University, Changsha, Hunan 410205, China
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    DOI: 10.3788/LOP57.041004 Cite this Article Set citation alerts
    Xiaowen Liu, Juncheng Lei, Yanpeng Wu. Synthetic Aperture Radar Target-Recognition Method Based on Bidimensional Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041004 Copy Citation Text show less
    Illustration of decomposition of SAR images via BEMD. (a) Original image; (b) BIMF at first level; (c) BIMF at second level; (d) BIMF at third level
    Fig. 1. Illustration of decomposition of SAR images via BEMD. (a) Original image; (b) BIMF at first level; (c) BIMF at second level; (d) BIMF at third level
    Illustration of classification principle of SVM
    Fig. 2. Illustration of classification principle of SVM
    Basic procedure of SAR target recognition method based on BEMD
    Fig. 3. Basic procedure of SAR target recognition method based on BEMD
    Optical and SAR images of ten targets in MSTAR data set. (a) BMP2; (b) BTR70; (c) T72; (d) T62; (e) BRDM2; (f) BTR60; (g) ZSU23/4; (h) D7; (i) ZIL131; (j) 2S1
    Fig. 4. Optical and SAR images of ten targets in MSTAR data set. (a) BMP2; (b) BTR70; (c) T72; (d) T62; (e) BRDM2; (f) BTR60; (g) ZSU23/4; (h) D7; (i) ZIL131; (j) 2S1
    Recognition results of proposed method under standard operating condition
    Fig. 5. Recognition results of proposed method under standard operating condition
    Recognition performances of proposed and compared methods at different pitch angles
    Fig. 6. Recognition performances of proposed and compared methods at different pitch angles
    Recognition performances of proposed and compared methods under noise interference
    Fig. 7. Recognition performances of proposed and compared methods under noise interference
    TypeBMP2BTR70T72T62BDRM2BTR60ZSU23/4D7ZIL1312S1
    Training232(Sn_9566)233231(Sn_812)299298256299299299299
    Test195(Sn_9563)196196(Sn_132)273274195274274274274
    Table 1. Training and test samples under standard operating condition
    TypeTrainingTest
    BMP2233(Sn_9563)196(Sn_9566)196(Sn_c21)
    BTR70233(Sn_c71)196(Sn_c71)
    T72232(Sn_132)195(Sn_812)191(Sn_s7)
    Table 2. Training and test samples with configuration variances
    TypePitchangle /(°)2S1BDRM2ZSU23/4T72
    Training15299298299299
    Test30288287288288
    45303303303303
    Table 3. Training and test samples with pitch angle difference
    MethodAverage recognition rate /%
    Proposed99.12
    SVM-PCA97.26
    SRC96.36
    CNN98.68
    Table 4. Recognition performance of proposed and compared methods under standard opearting condition
    MethodAverage recognition rate /%
    Proposed98.06
    SVM95.43
    SRC94.88
    CNN97.03
    Table 5. Recognition performance of proposed and compared methods under configuration variances
    Xiaowen Liu, Juncheng Lei, Yanpeng Wu. Synthetic Aperture Radar Target-Recognition Method Based on Bidimensional Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041004
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