• Infrared and Laser Engineering
  • Vol. 50, Issue 12, 20210165 (2021)
Yazhi Yang1 and Jun Li2、*
Author Affiliations
  • 1School of Computer Engineering, Chengdu Technological University, Chengdu 611730, China
  • 2Department of Education, Chengdu Technological University, Chengdu 611730, China
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    DOI: 10.3788/IRLA20210165 Cite this Article
    Yazhi Yang, Jun Li. Research on monogenic signal of application in infrared imagery target classification[J]. Infrared and Laser Engineering, 2021, 50(12): 20210165 Copy Citation Text show less
    Procedure of infrared image target classification based on joint representation of monogenic features
    Fig. 1. Procedure of infrared image target classification based on joint representation of monogenic features
    Illustration of the 10 targets in MWIR dataset
    Fig. 2. Illustration of the 10 targets in MWIR dataset
    Classification results of the proposed method for original test samples
    Fig. 3. Classification results of the proposed method for original test samples
    Comparison of performance of different methods on noisy test samples
    Fig. 4. Comparison of performance of different methods on noisy test samples
    Comparison of performance of different methods on occluded test samples
    Fig. 5. Comparison of performance of different methods on occluded test samples
    Method typeFeatureClassifierReference
    Reference 1Target contourGlobal similarity based on distance transform[8]
    Reference 2HOG descriptorsSVM[11]
    Reference 3Covariance descriptorKernel sparse coding[12]
    Reference 4Image pixelsCNN[15]
    Table 1. Descriptions of the reference methods
    Method typeAverage recognition rateEfficiency/ms
    Proposed method98.294.8
    Reference 194.4146.7
    Reference 295.187.3
    Reference 395.392.4
    Reference 497.4113.2
    Table 2. Comparison of performance of different methods on orginal test samples
    Yazhi Yang, Jun Li. Research on monogenic signal of application in infrared imagery target classification[J]. Infrared and Laser Engineering, 2021, 50(12): 20210165
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