Hierarchical Merging & Generalization Method of Three-Dimension City Model Group Based on the Theory of Spatial Visual Cognition
Hierarchical Merging & Generalization Method of Three-Dimension City Model Group Based on the Theory of Spatial Visual Cognition
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DOI：
10.4236/jgis.2019.112010
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Chaokui Li;Jianhui Chen;Jun Fang;Huiting Li;Pu Bu
中科院分区：
文献类型：
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作者：
Chaokui Li;Jianhui Chen;Jun Fang;Huiting Li;Pu Bu
为了简化三维建筑群模型，提出了一种基于视觉认知理论的聚类综合方法。该方法利用道路元素对场景进行粗略划分，然后利用方向、面积、高度等空间认知元素及其拓扑约束对场景进行精确分类，使其符合城市形态特征。使用Delaunay三角剖分网络和边界跟踪合成算法对模型进行合并和汇总，并对模型进行分层存储。提出的算法应该通过一个典型的城市复杂模型进行实验验证。实验结果表明，本文所用方法的效率比以前的方法提高了至少20%，并且随着测试数据量的增加，效率也在不断提高。分类结果符合人类的认知习惯，通过自适应控制聚类泛化过程中的各个阈值，可以相对统一不同模型的泛化程度。
In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.