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Road extraction method of high-resolution remote sensing image on the basis of the spatial information perception semantic segmentation model

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

  • Email：x18301096@stu.ahu.edu.cn
  • Introduction：吴强强，研究方向为深度学习遥感影像信息提取。E-mail：x18301096@stu.ahu.edu.cn
WU Qiangqiang1,  
  • Affiliation：

    School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China

WANG Shuai2,  
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

WANG Biao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

    Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China

  • Email：wuyanlan@ahu.edu.cn
  • Introduction：吴艳兰，研究方向为遥感大数据智能技术研究。E-mail：wuyanlan@ahu.edu.cn
WU Yanlan13*

Resümee

With the rapid development of remote sensing satellite technology, the automatic extraction of high-resolution remote sensing images has become a popular research direction in the field of remote sensing. Deep learning methods have been applied in remote sensing image road information extraction and achieved significant results. However, due to convolution and pooling and other operations in network, road extraction methods based on the deep learning have some problems, such as the loss of spatial features and ground object details and the frequent occurrence of false extraction during road extraction. In order to solve these problems, this paper designs an improved road extraction semantic segmentation network model to mitigate the impact of the above network structure.The proposed method is based on ResNet and introduces coordinate convolution and a global information enhancement module before and after the coding structure, respectively. First, the network structure is mainly composed of residual units of ResNet, which has powerful feature extraction and multiplexing capabilities, and extracts road features of different scales and levels. Second, coordinate convolution reduces the spatial information loss and enhances the edge information. The coordinate convolution before the coding structure introduces spatial coordinate information, which is beneficial to enhancing the extraction of effective spatial information. Finally, global pooling can improve global context awareness. The global information enhancement module after the coding structure can effectively extract global context information through global pooling, thereby improving the accuracy of road classification and reducing the influence of natural scene factors, such as houses and tree shadows, to a certain extent.In this paper, the Massachusetts Roads dataset was used in the experiment, and the results obtained exhibited good accuracy. The Recall rate was 71.02%, the comprehensive evaluation index (F1 Score) was 76.35%, and the IoU reached 62.18%. The F1 Score and IoU indicators of the proposed method are approximately 1% higher than those in U-net and D-LinkNet and exceed those of DeeplabV3+ and Segnet, which are lower than D-LinkNet in the recall index only.The comparison of the experimental results indicates that the proposed method can effectively alleviate the spatial feature and context information losses on the basis of the deep learning road extraction method and completely extract the roads in remote sensing images. Moreover, the proposed method can effectively extract the road in the case of trees and building shadows, and multi-scale roads can also be accurately extracted.

Schlüsselwort

deep learning;remote sensing image;road extraction;coordinate convolution;global information enhancement module

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