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Laser & Optoelectronics Progress, Volume. 55, Issue 2, 021001(2018)

Method of Vegetation Extraction Based on Deep Belief Network and Optimal Scale

Zujin Liu1, Ling Yang1、*, Zuhan Liu1,2, Linlin Duan1, Xianxian Qiao1, and Jiaojiao Gong1
Author Affiliations
  • 1 College of Environment and Planning, Henan University, Kaifeng, Henan 475004, China
  • 1 Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, Jiangxi 330022, China
  • 2 Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (18)
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    Figures & Tables(8)
    Schematic of DBN structure

    Fig. 1. Schematic of DBN structure

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    Schematic of RBM structure

    Fig. 2. Schematic of RBM structure

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    Flow chart of DBN classification

    Fig. 3. Flow chart of DBN classification

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    Flow chart of optimal segmentation scale calculation

    Fig. 4. Flow chart of optimal segmentation scale calculation

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    Classification accuracy of different DBN layers (experiments 1 and 3)

    Fig. 5. Classification accuracy of different DBN layers (experiments 1 and 3)

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    Classification accuracy of different DBN layers (experiments 2 and 4)

    Fig. 6. Classification accuracy of different DBN layers (experiments 2 and 4)

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    Classification maps by four different experimental methods based on DBN. (a) Classification map of DBN+9×9 neighborhood pixels; (b) classification map of DBN+9×9 neighborhood pixels+spectral-texture features; (c) classification map of DBN+optimal scale+9×9 neighborhood pixels; (d) classification map of DBN+optimal scale+spectral-texture features

    Fig. 7. Classification maps by four different experimental methods based on DBN. (a) Classification map of DBN+9×9 neighborhood pixels; (b) classification map of DBN+9×9 neighborhood pixels+spectral-texture features; (c) classification map of DBN+optimal scale+9×9 neighborhood pixels; (d) classification map of DBN+optimal scale+spectral-texture features

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    • Table 1. Various accuracies of different vegetation extraction methods

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      Table 1. Various accuracies of different vegetation extraction methods

      ExtractionmethodAccuracy
      Kappa coefficientOverall accuracyPAUAType
      DBN+9×9neighborhoodpixels0.87930.89870.94360.8856Tree
      0.89220.9381Grass
      0.83330.7653Bare soil
      0.93280.9680Road
      0.84350.8671Others
      DBN+9×9neighborhoodpixels+spectral-texture features0.8720.86240.96850.8865Trees
      0.86810.9864Grass
      0.54320.8800Bare soil
      0.99110.8793Road
      0.76320.8286Others
      DBN+optimalsegmentationscale+9×9neighborhood pixels0.86770.91920.94160.9644Trees
      0.93090.8686Grass
      0.57140.6667Bare soil
      0.91500.9790Road
      0.68180.5000Others
      DBN+optimalsegmentationscale+spectral-texture features0.61520.72960.79730.8732Tree
      0.73530.7453Grass
      0.40000.2128Bare soil
      0.89090.8167Road
      0.56410.2619Others
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    Zujin Liu, Ling Yang, Zuhan Liu, Linlin Duan, Xianxian Qiao, Jiaojiao Gong. Method of Vegetation Extraction Based on Deep Belief Network and Optimal Scale[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021001

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    Paper Information

    Category: Image processing

    Received: Jun. 26, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Ling Yang (yangling0606@163.com)

    DOI:10.3788/LOP55.021001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology