Extracting building patterns with multilevel graph partition and building grouping
Extracting building patterns with multilevel graph partition and building grouping
复制标题
通过多级图形分区和建筑分组提取建筑模式
DOI：
10.1016/j.isprsjprs.2016.10.001
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发表时间：
2016-12
影响因子：
12.7
通讯作者：
Du SH
中科院分区：
文献类型：
--
作者：
Du Shihong;Shu Mi;Luo Liqun;Cao Kai;Du SH
建筑模式对于城市景观评估、社会分析和多尺度空间数据自动生成至关重要。尽管已经进行了很多研究，但由于建筑图案类型学的不完整和提取方法的无效，仍然缺乏令人满意的结果。本研究旨在提供四种建筑模式（例如共线模式、曲线模式、平行和垂直组以及网格模式）的类型学，并提出四种有效且高效地提取这些模式的综合策略。首先，利用多级图划分方法，考虑面积、形状和视觉距离相似性，生成全局最优的建筑簇。在这一步中，使用Relief-F算法自动估计相似性度量的权重，而不是手动选择，从而获得高质量的聚类构建。其次，基于第一步产生的集群，提取策略根据邻近性、连续性和方向性标准将每个集群中的建筑物分组为模式。使用三个数据集测试所提出的方法。实验结果表明，所提出的方法可以产生令人满意的结果，并证明F-直方图模型比两种广泛使用的模型（即质心模型和Voronoi图）更好地表示建筑模式提取的相对方向。
Building patterns are crucial for urban landscape evaluation, social analyses and multiscale spatial data automatic production. Although many studies have been conducted, there is still lack of satisfying results due to the incomplete typology of building patterns and the ineffective extraction methods. This study aims at providing a typology with four types of building patterns (e.g., collinear patterns, curvilinear patterns, parallel and perpendicular groups, and grid patterns) and presenting four integrated strategies for extracting these patterns effectively and efficiently. First, the multilevel graph partition method is utilized to generate globally optimal building clusters considering area, shape and visual distance similarities. In this step, the weights of similarity measurements are automatically estimated using Relief-F algorithm instead of manual selection, thus building clusters with high quality can be obtained. Second, based on the clusters produced in the first step, the extraction strategies group the buildings from each cluster into patterns according to the criteria of proximity, continuity and directionality. The proposed methods are tested using three datasets. The experimental results indicate that the proposed methods can produce satisfying results, and demonstrate that the F-Histogram model is better than the two widely used models (i.e., centroid model and the Voronoi graph) to represent relative directions for building patterns extraction.
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DOI：
10.1109/isuma.1995.527776
发表时间：
1995-03
期刊：
Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society
影响因子：
--
作者：
J. Keller;Xiaomei Wang
通讯作者：
J. Keller;Xiaomei Wang
DOI：
10.1016/j.compenvurbsys.2009.07.005
发表时间：
2009-09
期刊：
Comput. Environ. Urban Syst.
影响因子：
--
作者：
Patrick Lüscher;R. Weibel;D. Burghardt
通讯作者：
Patrick Lüscher;R. Weibel;D. Burghardt
基于城市形态和格式塔理论的自动化建筑概括
DOI：
10.1080/13658810410001702021
发表时间：
2004-07-01
影响因子：
5.7
作者：
Li, Z;Yan, H;Chen, J
通讯作者：
Chen, J
影响因子：
1.1
作者：
N. Regnauld
通讯作者：
N. Regnauld
DOI：
--
发表时间：
2010
期刊：
--
影响因子：
--
作者：
J. Xia;Peijun Du;W. Cao
通讯作者：
J. Xia;Peijun Du;W. Cao