Volume 41 Issue 2
Feb.  2016
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FU Zhongliang, YANG Yuanwei, GAO Xianjun, ZHAO Xingyuan, LU Yuefeng, CHEN Shaoqin. Road Networks Matching Using Multiple Logistic Regression[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 171-177. DOI: 10.13203/j.whugis20150112
Citation: FU Zhongliang, YANG Yuanwei, GAO Xianjun, ZHAO Xingyuan, LU Yuefeng, CHEN Shaoqin. Road Networks Matching Using Multiple Logistic Regression[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 171-177. DOI: 10.13203/j.whugis20150112
shu

Road Networks Matching Using Multiple Logistic Regression

  • 1.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;

  • 2.

    School of Geosciences, Yangtze University, Wuhan 430100, China;

  • 3.

    School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China;

  • 4.

    Zhejiang Academy of Surveying and Mapping, Hangzhou 310012, China

Funds: The Natural Science Foundation of Shandong Province, No. ZR2014DL001.
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  • Received Date: May 28, 2015
  • Published Date: February 04, 2016
    Graphical Abstract
    • Abstract
  • Identifying corresponding objects is crucial in the process of heterogeneous road network matching. This paper proposed a road network matching method based on a multiple logistic regression algorithm. First, three dissimilar characteristics integrating both spatial and non-spatial features were used to describe the difference of the corresponding pairs of road objects;the minimum angle of the orientation, the mixed median Hausdorff distance, and semantic discrepancy. Using these three characteristics as variables of multiple logistic regression, we built a basic multiple logistic regression matching model. Samples to train the final road matching model were acquired to obtain matching results by predicting probability of each candidate road matching pair. Experimental results show that this method needs no exact feature weights and thresholds, and can solve the matching result problems stemming from over-reliance on single variable. This method has good adaptability, with higher precision and recall rates.
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    • References (12)
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