• Laser & Optoelectronics Progress
  • Vol. 58, Issue 2, 0228001 (2021)
Tianhao Ma1、2, Hai Tan2、*, Tianqi Li1、2, Yanan Wu1、2, and Qi Liu2
Author Affiliations
  • 1School of Geomatics, Liaoning Technical University, Fuxin, Liaoning 123000, China
  • 2Land and Resources Remote Sensing Application Center of the Ministry of Natural Resources, Beijing 100048, China
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    DOI: 10.3788/LOP202158.0228001 Cite this Article Set citation alerts
    Tianhao Ma, Hai Tan, Tianqi Li, Yanan Wu, Qi Liu. Road Extraction from GF-1 Remote Sensing Images Based on Dilated Convolution Residual Network with Multi-Scale Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0228001 Copy Citation Text show less
    Receptive field size of dilated convolution kernel. (a) Dilation rate is 1; (b) dilation rate is 2; (c) dilation rate is 4
    Fig. 1. Receptive field size of dilated convolution kernel. (a) Dilation rate is 1; (b) dilation rate is 2; (c) dilation rate is 4
    Road identification network structure designed in this paper
    Fig. 2. Road identification network structure designed in this paper
    Dilated convolution residual neural network structure
    Fig. 3. Dilated convolution residual neural network structure
    Training samples and labels. (a) Original image a; (b) label of image a; (c) original image b; (d) label of image b
    Fig. 4. Training samples and labels. (a) Original image a; (b) label of image a; (c) original image b; (d) label of image b
    Comparison of experimental results. (a) Input images; (b) ground truth; (c) extraction results of FCN-8s; (d) extraction results of SegNet; (e) extraction results of ResNet-101; (f) extraction results of our method
    Fig. 5. Comparison of experimental results. (a) Input images; (b) ground truth; (c) extraction results of FCN-8s; (d) extraction results of SegNet; (e) extraction results of ResNet-101; (f) extraction results of our method
    Enlarged display of experimental results. (a) Input images; (b) ground truth; (c) extraction results of ResNet-101; (d) extraction results of our method
    Fig. 6. Enlarged display of experimental results. (a) Input images; (b) ground truth; (c) extraction results of ResNet-101; (d) extraction results of our method
    MethodAccuracyPrecisionRecallF1IoU
    FCN-8s80.1679.8480.9878.2350.53
    SegNet90.5689.5891.2990.6360.31
    ResNet-10193.2192.3291.2490.4663.45
    Ours98.1196.5896.5396.5576.15
    Table 1. Quantitative evaluation of extraction accuracy of each method%
    Tianhao Ma, Hai Tan, Tianqi Li, Yanan Wu, Qi Liu. Road Extraction from GF-1 Remote Sensing Images Based on Dilated Convolution Residual Network with Multi-Scale Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0228001
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