| 利用多元Logistic回归进行道路网匹配 |
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| 引用本文： | 付仲良, 杨元维, 高贤君, 赵星源, 逯跃锋, 陈少勤. 利用多元Logistic回归进行道路网匹配[J]. 武汉大学学报 ( 信息科学版), 2016, 41(2): 171-177. DOI: 10.13203/j.whugis20150112 |
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| 作者姓名： | 付仲良 杨元维 高贤君 赵星源 逯跃锋 陈少勤 |
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| 作者单位： | 1.武汉大学遥感信息工程学院, 湖北武汉, 430079;;2.长江大学地球科学学院, 湖北武汉, 430100;;3.山东理工大学建筑工程学院, 山东淄博, 255049;;4.浙江省测绘科学技术研究院, 浙江杭州, 310012 |
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| 基金项目： | 山东省自然科学基金(ZR2014DL001)。 |
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| 摘 要： | 识别同名道路在多源异构道路网匹配过程中十分关键。提出了一种多元Logistic模型的道路网匹配算法。首先选取并设计了能有效综合空间与非空间信息进行道路不相似性描述与区分的三种特征,即最小方向变化角、综合中值Hausdorff距离和语义差异三种不相似性特征,然后利用此三项特征结合多元Logistic回归模型构建准确的道路网匹配模型。利用该模型对道路网中待匹配道路进行匹配概率预测,从而获取道路的匹配结果,实现路网匹配。实验结果表明,本文方法避免了组合特征精确权值与阈值的设定,并能有效解决匹配结果对单元变量过于依赖的问题,具有良好的适应性、较高的准确率和召回率。
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| 关 键 词： | 道路网匹配 地图合并 多元Logistic回归 综合中值Hausdorff距离 |
| 收稿时间： | 2015-05-29 |
Road Networks Matching Using Multiple Logistic Regression |
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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 |
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| Authors: | FU Zhongliang YANG Yuanwei GAO Xianjun ZHAO Xingyuan LU Yuefeng CHEN Shaoqin |
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| Affiliation: | 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 |
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| 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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| Keywords: | road networks matching map conflation multiple Logistic regression mixed mediam Hausdorff distance |
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