Author(s)

Chaokui Li^1,2*, Jianhui Chen^1,2, Jun Fang^1,2, Huiting Li^1,2, Pu Bu^1,2

¹National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan, China.
²Hunan Province Engineering Laboratory of Geospatial Information, Hunan University of Science and Technology, Xiangtan, China.

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.

Visual Cognition, 3D Building Model Group, Geometry Threshold, Hierarchical Generalization, Cluster Generalization

Li, C. , Chen, J. , Fang, J. , Li, H. and Bu, P. (2019) Hierarchical Merging & Generalization Method of Three-Dimension City Model Group Based on the Theory of Spatial Visual Cognition. Journal of Geographic Information System, 11, 124-137. doi: 10.4236/jgis.2019.112010.