测绘学报 ›› 2014, Vol. 43 ›› Issue (9): 960-968.doi: 10.13485/j.cnki.11-2089.2014.0125

• 学术论文 • 上一篇    下一篇

城市建筑群网格模式的图论识别方法

巩现勇1,2,武芳3   

  1. 1. 地理信息工程国家重点实验室
    2. 信息工程大学 地理空间信息学院
    3. 信息工程大学测绘学院三系GIS教研室
  • 收稿日期:2013-12-12 修回日期:2014-06-23 出版日期:2014-09-20 发布日期:2014-09-25
  • 通讯作者: 巩现勇 E-mail:gongxygis@whu.edu.cn
  • 基金资助:

    国家自然科学基金资助项目;地理信息工程国家重点实验室开放研究基金资助项目

The Graph Theory Approach to Grid Pattern Recognition in Urban Building Groups

GONG Xianyong1,2,3,WU Fang1,3   

  1. 1. Information Engineering University
    2. State Key Laboratory of Geo-information Engineering
    3. Information Engineering University
  • Received:2013-12-12 Revised:2014-06-23 Online:2014-09-20 Published:2014-09-25
  • Contact: GONG Xianyong E-mail:gongxygis@whu.edu.cn

摘要:

建筑群空间分布模式体现了城市的物质形式及其与社会经济功能之间的关系,反应了城市的空间结构特征,对于制图综合和多尺度表达等具有重要意义。结合国内外对该问题的研究,提出了基于图论的建筑群网格模式识别方法。首先分析研究了网格模式的认知特征和定义。然后利用Delaunay三角网构建建筑群的邻近关系,生成邻近图;从Gestalt视觉准则出发,基于三角剖分模型建立视觉距离;考虑直线模式的直线性、紧凑性等约束条件识别出交叉的多连通直线模式。最后对直线模式建立相交图和方向关系图,通过求解极大完全子图、连接、相交和后期修建等图运算,实现网格模式的识别。实验表明该方法能够识别出明显网格模式,其识别结果符合人类空间认知特点。

关键词: 制图综合, 建筑群, 网格模式, 模式识别, 图论

Abstract:

Map Patterns in building groups embody the relationship of the material form of cities and their social-economic function, and reflect the city’s spatial structure, which have great importance in Cartographic Generalization and Multi-Scale Representation. On the basis of related research home and abroad, a Graph Theory approach is proposed to recognize the grid pattern in building groups. Firstly the cognitive characteristics and definitions of grid pattern are analyzed. Then neighborhood relationship is captured by proximity graph with the help of Delaunay triangulation, and visual distance model is established considering the Gestalt principles. Multi-connected linear pattern is recognized with constrains such as linear arrangement and compactness. Finally the line pattern intersection graph and similar orientation graph are formed. The candidate grid patterns are extracted through graph operation such as finding maximal complete sub-graph, join, intersection and post-processing. Experiments show that this approach is effective, feasible and practicable for obvious grid pattern recognition with the agreement of human spatial cognitive characteristics.

Key words: Cartography generalization, building groups, grid pattern, pattern recognition, graph theory