سابِق|تال

Road extraction of high-resolution satellite remote sensing images in U-Net network with consideration of connectivity

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

  • Email：bz21422@163.com
  • Introduction：王斌，1996年生，男，硕士研究生，研究方向为遥感图像处理和地质大数据。E-mail：bz21422@163.com
WANG Bin1,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

CHEN Zhanlong1,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

WU Liang1,  
  • Affiliation：

    Xi 'an Research Institute of Surveying and Mapping, Xi 'an 710054, China

    State Key Laboratory of Geo-Information Engineering, Xi 'an 710054, China

XIE Peng23,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geomatics and Geoinformation,Guilin University of Technology, Guilin 541004, China

  • Email：bodhifan@163.com
  • Introduction：范冬林，1988年生，男，讲师，研究方向为图像智能解译。E-mail：bodhifan@163.com
FAN Donglin4*,  
  • Affiliation：

    College of Geomatics and Geoinformation,Guilin University of Technology, Guilin 541004, China

FU Bolin4

ملخص

Existing remote sensing image interpretation methods can obtain high-precision classification results but also have some defects. On the one hand, the problem of disconnection in the road extraction result of remote sensing image reduces the extraction precision. On the other hand, this problem affects the integrity of the road morphology. Thus, the extraction result cannot be directly applied to spatial decision making and analysis. Therefore, this study solves the problem of disconnection in the road extraction results of remote sensing images. A road extraction method for high-resolution remote sensing image of U-Net network with consideration of connectivity is also proposed.On the basis of the advantages of global feature representation of U-Net network in road extraction of high-resolution remote sensing images, this study proposes a broken road repair method that considers connectivity to improve the local features of U-Net network. First, the sample data after data enhancement and data volume expansion are used as an input of the U-Net network to train the model and perform road extraction of the optimal model. Then, the road breakpoint detection, the road breakpoint clustering, and the cubic polynomial curve fitting are organically combined to optimize the result because of the broken road appearing in the extraction result.The proposed method is practical and feasible according to the verification of experimental results. The method is also universal. The experiment shows that the accuracy and shape integrity of the road extraction in this method are improved significantly compared with similar networks. The precision is 86.25%, the recall rate is 85.50%, and F1-score reaches 85.87%.The roads extracted by the proposed method have good connectivity and morphological integrity. The resulting image can be directly applied to geographic decision analysis, especially for post-disaster path planning. The proposed method has certain reference significance for the occurrence of similar disconnection problems in the classification results of linear objects, such as roads, power grids, orbits, and rivers.

مفهوم

remote sensing;the integrity of the road morphology;U-Net network;high-resolution remote sensing images;connectivity;broken road

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