地图学与地理信息

居民地要素化简的形状识别与模板匹配方法

  • 晏雄锋 ,
  • 艾廷华 ,
  • 杨敏
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  • 武汉大学资源与环境科学学院, 湖北 武汉 430079
晏雄锋(1990-),男,博士生,研究方向为空间数据匹配及更新。E-mail:xiongfeng.yan@whu.edu.cn

收稿日期: 2015-03-26

  修回日期: 2016-05-16

  网络出版日期: 2016-07-28

基金资助

国家自然科学基金重点项目(41531180);国家863计划(2015AA1239012);数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放基金(DM2016SC05)

A Simplification of Residential Feature by the Shape Cognition and Template Matching Method

  • YAN Xiongfeng ,
  • AI Tinghua ,
  • YANG Min
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  • School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

Received date: 2015-03-26

  Revised date: 2016-05-16

  Online published: 2016-07-28

Supported by

The National Natural Science Foundation of China(No.41531180);The National High Technology Research and Development Program of China(863 Program)(No.2015AA1239012);Funded by National Administration of Surveying, Mapping and Geo-information Engineering Laboratory in Digital Cartography and Land Management(No.DM2016SC05)

摘要

针对居民地要素的分布和表达具有典型模板化特点,本文通过对其形状结构和区域环境进行分析,运用形态抽象概括和区域环境典型化基本原则构建一批模板作为居民地目标化简与典型化表达的候选形状,并基于转角函数的形状描述算子,计算居民地目标与模板之间的相似性程度。该方法从形状认知的角度出发,通过寻找与目标形状结构特征相似的模板替换原目标来完成化简操作,能较好地保持居民地目标的整体形状结构特征,同时兼顾了综合前后的面积均衡。通过真实数据进行试验,结果表明该方法具有较强的可靠性和实用性,可规模化应用于地形图上的地图综合实践。

本文引用格式

晏雄锋 , 艾廷华 , 杨敏 . 居民地要素化简的形状识别与模板匹配方法[J]. 测绘学报, 2016 , 45(7) : 874 -882 . DOI: 10.11947/j.AGCS.2016.20150162

Abstract

Aiming at the typical template characteristics of building representation, this study built a series of templates to abstract the building shape by generalizing building polygons and analyzing the typical characteristics of regional environment.The shape description operator used the turn function method through the measure of the similarity between the building target and the template. From the perspective of shape cognition, this method conducted the building simplification by searching and matching the most similar template to replace the target building. The presented method is able to guarantee the overall shape structure unchanging and maintaining the area balance after the simplification. The experiments under real data show that the method holds high reliability and practicability, able to be widely applied to practical map generalization.

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