• Laser & Optoelectronics Progress
  • Vol. 58, Issue 16, 1628004 (2021)
Jing Lu1、2, Jiuying Chen1、2、*, Wei Li1, Mei Zhou1, Jian Hu1, Wenxin Tian1, and Chuanrong Li1
Author Affiliations
  • 1Key Laboratory of Quantitative Remote Sensing Information Technology of CAS, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2College of Optoelectronics, Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.3788/LOP202158.1628004 Cite this Article Set citation alerts
    Jing Lu, Jiuying Chen, Wei Li, Mei Zhou, Jian Hu, Wenxin Tian, Chuanrong Li. Research on Classification of Pest and Disease Tree Samples Based on Hyperspectral Lidar[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1628004 Copy Citation Text show less
    Schematic of hyperspectral lidar
    Fig. 1. Schematic of hyperspectral lidar
    Sample example of hyperspectral lidar data acquisition
    Fig. 2. Sample example of hyperspectral lidar data acquisition
    Reflectivity of surface of healthy and infected samples. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata paniculata
    Fig. 3. Reflectivity of surface of healthy and infected samples. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata paniculata
    Classification accuracy trend of healthy tree samples and infected tree samples under different σ values. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata
    Fig. 4. Classification accuracy trend of healthy tree samples and infected tree samples under different σ values. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata
    Classification accuracy trend of healthy tree samples and infected tree samples under different C values. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata
    Fig. 5. Classification accuracy trend of healthy tree samples and infected tree samples under different C values. (a) Ailanthus altissima; (b) Pinus yunnanensis; (c) Koelreuteria paniculata
    ParameterValue
    Spectral range/nm400--2400
    Spectral resolution/nm2--10
    Beam divergence/mmrad0.4
    Beam diameter/mm10
    Table 1. Main parameters of hyperspectral lidar
    Test set sampleParameterClassification accuracy
    Ailanthus altissimaσ=2-2.7, C=470.9698
    Pinus yunnanensisσ=2-2.1, C=180.9121
    Koelreuteria paniculataσ=2-2.5, C=480.6621
    Table 2. Classification accuracy of test set sample data
    Jing Lu, Jiuying Chen, Wei Li, Mei Zhou, Jian Hu, Wenxin Tian, Chuanrong Li. Research on Classification of Pest and Disease Tree Samples Based on Hyperspectral Lidar[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1628004
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