标题:Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features
作者:Li, Chengming; Yin, Yong; Wu, Pengda; Wu, Wei
作者机构:[Li, Chengming; Yin, Yong; Wu, Pengda] Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China.; [Yin, Yong; Wu, Wei] Shandong Univ Sci & 更多
通讯作者:Wu, PD
通讯作者地址:[Wu, PD]Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China.
来源:ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版年:2019
卷:8
期:7
DOI:10.3390/ijgi8070303
关键词:map generalization; densely junction areas; skeleton line extraction;; stroke features
摘要:Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.
收录类别:SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069558355&doi=10.3390%2fijgi8070303&partnerID=40&md5=4ce2a3a2137f96f020d425ba69a58eea
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