Optics and Precision Engineering, Volume. 32, Issue 10, 1552(2024)

Optical remote sensing road extraction network based on GCN guided model viewpoint

Guanghui LIU1...2,*, Zhe SHAN1,2, Yuanhai YANG1,2, Heng WANG1,2, Yuebo MENG1,2,*, and Shengjun XU12 |Show fewer author(s)
Author Affiliations
  • 1College of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an70055,China
  • 2Xi'an Key Laboratory of Intelligent Technology for Building and Manufacturing,Xi'an710055,China
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    Figures & Tables(13)
    Overall structure of the RGGVNet
    Overall structure of the ConvNeXt
    Illustration of the GVPG module
    Illustration of the dense guidance viewpoint strategy
    Overall structure of the decoder
    Sample images and labels of datasets
    Visualized results of different algorithms on the Massachusetts road dataset
    Visualized results of different algorithms on the DeepGlobe road dataset
    Test results in wider cities
    • Table 1. Results of comparative experiments on the Massachusetts road dataset

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      Table 1. Results of comparative experiments on the Massachusetts road dataset

      MethodsPrecision/%Recall/%F1-score/%IoU/%
      FCN69.1670.1469.6553.43
      SegNet75.7678.2676.9961.21
      PSPNet76.3678.1677.2461.37
      U-Net76.1279.0177.5462.52
      LinkNet81.5080.6377.0762.70
      DeeplabV3+74.4078.8576.5661.02
      CDG81.4171.8076.1061.90
      DA-RoadNet79.1677.2578.1961.90
      CADUNet79.4576.5577.8964.19
      DDU-Net82.5473.9978.0363.98
      RGGVNet77.0281.9379.4065.84
    • Table 2. Results of comparative experiments on the DeepGlobe road dataset

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      Table 2. Results of comparative experiments on the DeepGlobe road dataset

      MethodsPrecisionRecallF1-scoreIoU
      FCN71.3874.7473.0257.51
      SegNet69.4980.0173.2260.43
      PSPNet67.6480.9273.6959.82
      U-Net81.5080.4080.9567.99
      LinkNet77.4579.9878.6864.86
      DeeplabV3+68.7280.4874.1461.02
      D-LinkNet64.12
      RADANet73.6758.58
      BDTNet84.1876.7780.3067.09
      DCS-TransUperNet82.4478.4380.3965.36
      SDUNet78.4074.2079.4066.80
      CoANet81.2268.37
      RENA78.4077.0076.4063.10
      RGGVNet79.5884.3781.9069.36
    • Table 3. Results of ablation experiment

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      Table 3. Results of ablation experiment

      MethodF1-score/%IoU/%Params.(×106
      Baseline75.8961.5429.663
      Baseline-M76.8762.7930.017
      Baseline-ML77.0163.1230.017
      Baseline-MG78.8865.5533.582
      Baseline-MGS79.1765.7033.582
      Baseline-MGSL79.4065.8433.582
    • Table 4. Experimental results of loss function hyperparametric

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      Table 4. Experimental results of loss function hyperparametric

      αF1-score/%IoU/%
      079.1765.70
      0.279.3365.80
      0.479.4065.84
      0.679.1365.81
      0.878.9765.74
      1.078.6365.48
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    Guanghui LIU, Zhe SHAN, Yuanhai YANG, Heng WANG, Yuebo MENG, Shengjun XU. Optical remote sensing road extraction network based on GCN guided model viewpoint[J]. Optics and Precision Engineering, 2024, 32(10): 1552

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    Paper Information

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    Received: Nov. 14, 2023

    Accepted: --

    Published Online: Jul. 8, 2024

    The Author Email: LIU Guanghui (guanghuil@163.com), MENG Yuebo (mengyuebo@163.com)

    DOI:10.37188/OPE.20243210.1552

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