Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (1): 200-206.DOI: 10.3778/j.issn.1002-8331.1911-0169

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Multi-feature High-Resolution Remote Sensing Road Extraction Based on PPMU-net

ZHANG Yonghong, YAN Bin, TIAN Wei, WANG Jiangeng   

  1. 1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
    2.School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
    3.School of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Online:2021-01-01 Published:2020-12-31



  1. 1.南京信息工程大学 自动化学院,南京 210044
    2.南京信息工程大学 计算机与软件学院,南京 210044
    3.南京信息工程大学 大气科学学院,南京 210044


To solve the problem of unrepresentative selection of road features and low segmentation accuracy under complex terrain conditions, a high resolution remote sensing road extraction method based on the convolutional neural network(PPMU-net) is proposed. Firstly, the Gaofen-2 satellite spectral information of the three channels and the corresponding topographic information(slope, aspect and dem) are integrated into multi-feature to synthesize the remote sensing image of the six channels. Secondly, the multi-feature remote sensing images are cut and the images containing roads are screened by Convolutional Neural Network(CNN). Finally, the remote sensing images containing only roads are sent to PPMU-net for training, and the road extraction model of high-resolution remote sensing images is constructed. Compared with U-Net and PSPNet, the proposed method can achieve better results in the extraction of high-resolution remote sensing roads and improve the accuracy of road segmentation under complex terrain conditions.

Key words: Gaofen-2 satellite, PPMU-net Neural Network, multi-features fusion, complex terrain



关键词: 高分二号, PPMU-net神经网络, 多特征, 复杂地形