• Optoelectronic Technology
  • Vol. 40, Issue 1, 6 (2020)
Wei XIONG1、2、3, Laifu GUAN1, Lei TONG1, Chuansheng WANG1, Min LIU1、2, and Chunyan ZENG1、2
Author Affiliations
  • 1School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, CHN
  • 2Hubei Collaborative Innovation Center for High⁃Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, CHN
  • 3Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
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    DOI: 10.19453/j.cnki.1005-488x.2020.01.002 Cite this Article
    Wei XIONG, Laifu GUAN, Lei TONG, Chuansheng WANG, Min LIU, Chunyan ZENG. Research on Road Extraction Algorithm Based on Residual Neural Networks[J]. Optoelectronic Technology, 2020, 40(1): 6 Copy Citation Text show less

    Abstract

    In order to solve the problem of road extraction information loss in remote sensing images, a road extraction algorithm based on residual neural networks was proposed. Firstly, an encoder-decoder network was constructed, combined with pre-coder and dilated convolution module to extract more semantic information.Secondly, the parallel designed dilated convolution module was added to the middle part of the encoder-decoder structure, which could extract features of different receptive field features. Finally, the encoder-decoder used jumper to perform multi-scale feature fusion, learning more low-dimensional and high-dimensional features.In the Massachusetts road dataset, this method had 11 %, 0.3 %, and 7.4 % improvement in Precision, Recall, and F1-score performance indicators. At the same time, it also achieved 97.9 % in the Accuracy index. Compared with other algorithms, the algorithm has certain application value.
    Wei XIONG, Laifu GUAN, Lei TONG, Chuansheng WANG, Min LIU, Chunyan ZENG. Research on Road Extraction Algorithm Based on Residual Neural Networks[J]. Optoelectronic Technology, 2020, 40(1): 6
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