A method for the extraction of partition lines from long and narrow patches that account for structural features

Abstract

The extraction of partition lines for long and narrow patches (LN patches) is an important yet difficult problem in the generalization of thematic data. When current methods are used to process polygons with irregular shapes or complex branch convergence zones, the extracted line structural features tend to be inaccurate and topologically erroneous. In this article, we propose an improved partition lines extraction algorithm of constrained Delaunay triangulation to counter these issues. The proposed method aims to maintain consistency between the extracted line structure characteristics and the actual object structure, especially for complex branch convergence zones. First, we describe three types of aggregation patterns (Type A, B, and C aggregation zones) that occur in partition line extractions for LN patches of complex branch convergence zones using Delaunay triangulation. Then, a partition line extraction algorithm that accounts for the direction between the edges of triangles and the distance of nodes in aggregation zones is proposed. Finally, we test our method for a dataset relating to Guizhou Province, China. Compared with the current method that uses quantitative indicators and visualization, the results indicate that our method not only has applicability for simple situations but also is superior for preserving structural features of complex branch convergence zones.

Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features

“…Figure 9 shows complex junction areas with uneven boundaries and the branching water is arranged irregularly and has inconsistent extension directions. It can be seen from Figure 8 that for the simple junction areas, the method of Li et al [17] is basically consistent with the results of the method proposed in this paper, which is able to well remove the jitters at junction areas and more accurately reflect the main structure and extension features of water elements. However, for the areas with irregular and complex junctions, as shown by the rectangles A, B, and C in Figure 8a, the method of Li et al [17] depends on the fitting distance threshold and the direction threshold, which leads to the skeleton line having a certain degree of deviation, as shown by the rectangles A, B, and C in Figure 8b.…”

“…Li et al [17] makes a full analysis of the existing skeleton line extraction methods based on Delaunay triangulation and proposes an optimized algorithm for the dissolving operation of long and narrow patches in land-cover data. The basic idea is to introduce a constrained Delaunay triangulation, identify the junction areas based on the degree of node correlation and eliminate the jitter on the skeleton line of the junction areas under direction and intersection criterion constraints.…”

“…To verify the reliability and effectiveness of the proposed method, it is compared with the skeleton line extraction method of Li et al [17]. The overall information of the processing area using the method of this paper is shown in Table 2.…”

“…Therefore, the extracted skeleton line is smoother and more natural, as shown by rectangles A, B, and C in Figure 8c. It can be seen from Figure 9 that for complex areas with dense junctions, the method of Li et al [17] is subject to severe interference of the complex boundaries and arrangement structure of the branch; it is unable to process the skeleton line jitter of this area, and the extracted skeleton line has a large degree of distortion and thus loses the overall structure of this region, as shown by the rectangles A in Figure 9(b). In contrast, the method proposed in this paper can better extract the main structure of this region and more accurately describe the skeleton line of the main structure.…”

“…Haunert et al [16] studied a large amount of road data and found that existing methods have difficulty maintaining the main structure and extension characteristics of polygons with irregular and complex junctions. Li et al [17] made a preliminary exploration of skeleton line extraction in certain areas with many junctions; however, their method relies on the skeleton line direction and intersection criterion in the junction area and is thus still unable to handle complex areas. 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.…”