特征分类与邻近图相结合的建筑物群空间分布特征提取方法

doi:10.11947/j.AGCS.2017.20160374

测绘学报 ›› 2017, Vol. 46 ›› Issue (5): 631-638.doi: 10.11947/j.AGCS.2017.20160374

特征分类与邻近图相结合的建筑物群空间分布特征提取方法

郭庆胜^1,2, 魏智威¹, 王勇³, 王琳¹

1. 武汉大学资源与环境科学学院, 湖北武汉 430079;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
3. 中国测绘科学研究院, 北京 100830

收稿日期:2016-07-25 修回日期:2017-01-13 出版日期:2017-06-20 发布日期:2017-06-05
作者简介:郭庆胜(1965-),男,博士,教授,博士生导师,研究方向为地图制图综合、地理信息智能化处理与可视化。E-mail:guoqingsheng@whu.edu.cn.com
基金资助:
国家自然科学基金（41471384）；公益性科研专项（201512032）

The Method of Extracting Spatial Distribution Characteristics of Buildings Combined with Feature Classification and Proximity Graph

GUO Qingsheng^1,2, WEI Zhiwei¹, WANG Yong³, WANG Lin¹

1. School of Resources and Environment Science, Wuhan University, Wuhan 430079, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
3. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received:2016-07-25 Revised:2017-01-13 Online:2017-06-20 Published:2017-06-05
Supported by:
The National Natural Science Foundation of China (No. 41471384);Special Fund for Research in the Public Interest (No.201512032)

摘要/Abstract

摘要： 建筑物群综合过程中需要对建筑物群空间分布特征进行认知和识别。本文在分析国内外相关研究的基础上，从描述建筑物空间特征的大量指标中，利用主成份分析方法，总结并提出了有代表性的建筑物空间特征指标集：凸包面积、紧密度IPQ指标、边数和最小面积外接矩形方向，并基于这些指标研究了建筑物群的分类。在利用最小生成树邻近图（MST）划分建筑物空间子群时，考虑了建筑物成群与所处地理环境（河流和道路等因素）的关系。另外，基于最邻近图（NNG）、MST、相对邻近图（RNG）和Gabriel图（GG）4种建筑物群邻近图，提出了自动识别具有特定空间排列建筑物子群的方法，并比较分析了识别结果的影响因素和可用性。最后，选择北京某地区建筑物群为试验对象，实现了对建筑物群的分类和空间聚类，并提取了其中直线型空间排列的建筑物子群。

关键词: 地图综合, 建筑物群, 分类, 空间聚类, 空间排列

Abstract: Spatial distribution characteristics of building clusters should be recognized and detected in generalization of building clusters. Based on analysis of relevant research at home and abroad, four major measures(area of the convex hull, compactness, number of edges, orientation of the smallest minimum bounding rectangle) are summarized and put forward from the existing measures with the help of principal component analysis. According to these selected measures, the building classification are studied. When MST(minimum spanning tree) is used to partition the building clusters, factors such as rivers and roads are taken into consideration. Furthermore, a method detecting linear patterns in building clusters automatically is proposed by means of NNG(nearest neighborhood graph), MST, RNG(relative neighborhood graph) and GG(Gabriel graph). Then the influence factors and usability about the recognized results are analysed. Finally, a part of map from OSM (open street map) in Beijing is chosen as experimental data, classification and clustering of the buildings are realized, and the linear patterns in the sub-clusters are recognized.

Key words: map generalization, building clusters, classification, spatial clustering, spatial pattern

中图分类号:

P208

郭庆胜, 魏智威, 王勇, 王琳. 特征分类与邻近图相结合的建筑物群空间分布特征提取方法[J]. 测绘学报, 2017, 46(5): 631-638.

GUO Qingsheng, WEI Zhiwei, WANG Yong, WANG Lin. The Method of Extracting Spatial Distribution Characteristics of Buildings Combined with Feature Classification and Proximity Graph[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 631-638.

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特征分类与邻近图相结合的建筑物群空间分布特征提取方法

The Method of Extracting Spatial Distribution Characteristics of Buildings Combined with Feature Classification and Proximity Graph

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