Abstract
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.
Highlights
Ai’s studies [1] have pointed out that the extraction of skeleton lines is a key step to realize map generalization operations such as polygon collapse and dissolving
The structure of the article is as follows: Section 2 provides related work on the skeleton lines extraction method based on Delaunay triangulation; Section 3 presents the method for extracting skeleton lines in areas with dense junctions considering the stroke feature; Section 4 provides a series of experiments that were conducted to validate the reliability and superiority of the proposed method; Section 5 discusses conclusions and future works
Relying on the WJ-III map workstation developed by the Chinese Academy of Surveying and Mapping [20], the method of extracting the skeleton line of the areas with dense junctions considering stroke features proposed in this paper is embedded, and a complex water area group in the topographic map of an area in Jiangsu with a scale of 1:10000 was taken as the experimental data for reliability and effectiveness verification
Summary
Ai’s studies [1] have pointed out that the extraction of skeleton lines is a key step to realize map generalization operations such as polygon collapse and dissolving. DeLucia et al [10] first proposed a skeleton line extraction method based on boundary-constrained Delaunay triangulation (CDT). In the process of research, some scholars found that at the skeleton line extracted by CDT, there existed jitters at the branch junctions and the polygon boundary. Jones et al [13], Uitermark et al [14] and Penninga et al [15] proposed using the branch skeleton line direction, boundary simplification, densification boundary nodes and other methods to modify the skeleton lines. This paper will focus on an approach for the skeleton line extracting in areas with dense junctions, and aims to propose a new method that may identify the complex junction areas automatically and obtain the skeleton lines fitting in with human visual cognition in the process of map generalization. The structure of the article is as follows: Section 2 provides related work on the skeleton lines extraction method based on Delaunay triangulation; Section 3 presents the method for extracting skeleton lines in areas with dense junctions considering the stroke feature; Section 4 provides a series of experiments that were conducted to validate the reliability and superiority of the proposed method; Section 5 discusses conclusions and future works
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