| 顾及多空间相似性的地下管线数据匹配 |
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| 引用本文： | 龚敏霞,袁赛,储征伟,张书亮,房彩丽. 顾及多空间相似性的地下管线数据匹配[J]. 测绘学报, 2015, 44(12): 1392-1400. DOI: 10.11947/j.AGCS.2015.20150207 |
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| 作者姓名： | 龚敏霞 袁赛 储征伟 张书亮 房彩丽 |
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| 作者单位： | 1. 南京师范大学虚拟地理环境教育部重点实验室, 江苏 南京 210023;2. 江苏省国土资源信息中心, 江苏 南京 210017;3. 南京师范大学强化培养学院, 江苏 南京 210023;4. 南京市测绘勘察研究院有限公司, 江苏 南京 210019;5. 江苏省地理信息资源开发与利用协同创新中心, 江苏 南京 210023 |
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| 基金项目： | 国家自然科学基金(41171301)，江苏高校优势学科建设工程资助项目，The National Natural Science Foundation of China(41171301)，A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions |
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| 摘 要： | 综合地下管线与专业地下管线是同一管线地物的两种表达形式。综合地下管线数据精准、概括;专业地下管线数据表达范围广、属性信息翔实。本文以天然气管线数据为例,选取管点关联管段分布形态作为衡量依据计算管线结构相似性,从管点本体概念名称和属性两方面计算管线语义相似性,以两管点间管段分布形态为特征计算管线形状相似性。以此空间相似性构建管点特征向量,采用SVM(support vector machine)支持向量机的分类方法及管点唯一匹配原则实现管点实体匹配。试验表明该算法能够有效解决管点匹配问题。
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| 关 键 词： | 综合管线 专业管线 数据匹配 空间相似性 SVM支持向量机 |
| 收稿时间： | 2015-04-20 |
| 修稿时间： | 2015-10-15 |
Underground Pipeline Data Matching Considering Multiple Spatial Similarities |
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| GONG Minxia,YUAN Sai,CHU Zhengwei,ZHANG Shuliang,FANG Caili. Underground Pipeline Data Matching Considering Multiple Spatial Similarities[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12): 1392-1400. DOI: 10.11947/j.AGCS.2015.20150207 |
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| Authors: | GONG Minxia YUAN Sai CHU Zhengwei ZHANG Shuliang FANG Caili |
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| Abstract: | Integrated and professional underground pipeline data are two forms of pipeline.Integrated underground pipeline data is accurate and general, while professional underground pipeline data expresses and contains detailed attribute information.Taking the data of natural gas pipeline as an example, this paper calculates structural similarity measured by the distribution pattern of pipelines that pipeline-point connects with, semantic similarity presented by the names and attributes of the pipeline-point ontology concept, and shape similarity characterized by the shape of arcs between two pipeline-points. The matching of pipe points is realized with the method of support vector machine classification algorithm and unique-matching principle combined with these spatial similarity. Test results show the matching of pipe points is well solved by the proposed algorithm. |
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| Keywords: | integrated pipeline professional pipeline data matching spatial similarity support vector machine |
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