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Road extraction in rural areas from high resolution remote sensing image using a improved Full Convolution Network

  • role： First author第一作者
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

  • Email：chkl_hn@163.com
  • Introduction：李朝奎，1967年生，男，教授，研究方向为三维地理建模及其应用。E-mail： chkl_hn@163.com
LI Chaokui12,  
  • Affiliation：

    Remote Sensing Application Promotion Department of Hunan Second Institute of Surveying and Mapping, Changsha 410000, China

ZENG Qiangguo3,  
  • role： Corresponding author通信作者
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

  • Email：fangjun008@hnust.edu.cn
  • Introduction：方军，1985年生，男，讲师， 研究方向为城市空间分析与建模。E-mail： fangjun008@hnust.edu.cn
FANG Jun12*,  
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

WU Nei12,  
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

WU Kaihua12

реферат

Aiming at the problems of limited research, application of extracting rural roads with high-resolution remote sensing data, and insufficient accuracy of extraction results, a new improved full convolution rural road extraction network model Distributed Convolution network (DC net) is proposed; it combines void convolution and Air Spatial Pyramid Pooling (ASPP) structure. The model extracts the depth feature information of the road based on the full convolution encoding and decoding structures. At the same time, in accordance with the characteristics of the slender rural roads, the ASPP structure based on the hollow convolution is added between the decoded layers to extract the multiscale characteristic information of the road, and the Field of View (FOV) is expanded without sacrificing the spatial resolution of the feature, thereby improving the recognition rate of narrow and fine rural roads. Some suburban areas of Changzhutan city group are considered the experimental objects and the domestic satellite remote sensing image of Gaogaoer as the experimental data. Experimental results are compared with those of the classical methods of all convolution networks. The results show that: (1) the proposed road extraction model DC net is feasible in rural road extraction, with the overall extraction average accuracy reaching 98.72%, indicating high extraction accuracy; (2) comparative results of the effect of several classic full convolution network models on rural road extraction, DC net extraction accuracy and connectivity, as well as tree and shadow shading in the aspect of block are acceptable; (3) the improved road extraction model of the entire proposed convolution network can effectively extract the feature information of rural roads in high-resolution remote sensing images. The overall extraction effect is improved; it provides a new approach for improving the precision of rural road extraction based on domestic high-resolution images.Based on the full convolution network model in deep learning, this paper proposes an improved full convolution rural road extraction model DC net which combines hole convolution and ASPP structure. According to the characteristics of long and thin and connectivity of rural roads, this method combined with hole convolution to expand the receptive field of feature map in the process of model training, which makes the extraction of rural roads more complete.

ключеви́че слова́

Full Convolution Network;Dilated Convolution Network;Atrous convolution;ASPP;Rural road extraction

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