Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1628002(2023)

Small Water Body Extraction Based on GF-2 Image

Rujun Chen1, Yunwei Pu1,2、*, Jiahou Zhou1, Jun Li1, and Xuefeng Wang3
Author Affiliations
  • 1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan, China
  • 2Compute Center, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • 3Puer 3d Mapping Engineering Co., Ltd., Puer665000, Yunnan, China
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    Figures & Tables(8)
    Schematic of the study area
    Analysis between brightness value and gray scale value at each band and SWI analysis diagram. (a) Analysis diagram between brightness value and gray scale value at green band (G); (b) analysis diagram between brightness value and gray scale value at red band (R); (c) analysis diagram between brightness value and gray scale value at blue band (B); (d) analysis diagram between brightness value and gray scale value at near-infrared band (NIR); (e) SWI analysis diagram
    Overlay image results after different algorithms distinguish water bodies from shadows and other ground objects. (a) Manual labeled shadow and water body; (b) LGR segmentation result; (c) SWI segmentation result
    Technology roadmap
    Water extraction results of different methods. (a) Decision tree; (b) SVM; (c) RF; (d) CNN; (e) NDWI+NIR; (f) proposed method; (g) manual visual interpretation
    • Table 1. Technical indexes of payload of GF-2 satellite

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      Table 1. Technical indexes of payload of GF-2 satellite

      Sensor typeBand numberWavelength /μmSpatial resolution/m
      Panchromatic camera(2 sets)10.45-0.901(subsatellite point 0.81)
      Multispectral camera(2 sets)4B:0.45-0.524(subsatellite point 3.24)
      G:0.52-0.59
      R:0.63-0.69
      NIR:0.77-0.89
    • Table 2. Water extraction area and extraction difference

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      Table 2. Water extraction area and extraction difference

      ParameterDecision treeSVMRFNDWI+NIRCNNProposed methodManual visual interpretation
      Area /m²6627652616865562373377141186662818266461076731631
      Extraction difference /m2104006562976494294409555103449855240
      Difference ratio /%1.558.367.346.081.541.20
    • Table 3. Water extraction accuracy of various methods

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      Table 3. Water extraction accuracy of various methods

      MethodPA /%UA /%Hellden accuracy /%Short accuracy /%Kappa coefficient /%
      Decision tree90.2598.594.289.0288.85
      SVM88.7995.8392.1685.4987.15
      RF90.2198.4594.1588.9588.8
      NDWI+NIR92.787.5190.0381.8791.46
      CNN93.449594.289.0692.42
      Proposed method95.5697.4196.4793.294.86
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    Rujun Chen, Yunwei Pu, Jiahou Zhou, Jun Li, Xuefeng Wang. Small Water Body Extraction Based on GF-2 Image[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1628002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 7, 2022

    Accepted: Nov. 24, 2022

    Published Online: Aug. 18, 2023

    The Author Email: Pu Yunwei (puyunwei@126.com)

    DOI:10.3788/LOP222488

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