引用本文: 陆毅, 翟京生, 杜景海, 李树军. 数字海图点群状特征的识别、量测与综合[J]. 武汉大学学报 ( 信息科学版), 2001, 26(2): 133-139.
LU Yi, ZHAI Jingsheng, DU Jinghai, LI Shujun. Recognition,Measurement and Generalization for Point Cluster Features in Digital Nautical Chart[J]. Geomatics and Information Science of Wuhan University, 2001, 26(2): 133-139.
 Citation: LU Yi, ZHAI Jingsheng, DU Jinghai, LI Shujun. Recognition,Measurement and Generalization for Point Cluster Features in Digital Nautical Chart[J]. Geomatics and Information Science of Wuhan University, 2001, 26(2): 133-139.

## Recognition,Measurement and Generalization for Point Cluster Features in Digital Nautical Chart

• 摘要: 空间分布特征的保持是点群自动综合的核心和难点所在,为此,本文定义了4个参量:分布范围、分布密度、分布中心和分布轴线,来描述点群目标的结构化信息。利用Delaunay三角网和Voronoi图两种模型,着重探讨了点群分布特征的识别和量测问题,并在识别与量测的基础上,通过Voronoi图的动态构建,给出了点群自动综合模型,通过实际岛群数据的检测,证明了模型的正确性与可行性。

Abstract: This paper,based on the Delaunay triangulation and Voronoi diagram model,focuses on the discussion of spatial distribution properties by recognition and measurement.Four characteristic parameters are defined for distribution property description:distribution density of three dimensions,distribution range of two dimensions,distribution axis of one dimension,distribution center of zero dimension.With the aid of Delaunay triangulation and Voronoi diagram,description and calculation models of above-mentioned parameters are established. Firstly,considering the visual principles fully,a new method,which finds the distribution polygon range by "nibbling the outside triangles",is presented.Furthermore,different results can be gained by using different threshold values,so the continuous-scale display may become real. Secondly,the distribution density is represented by Voronoi cell size and visualized as gray image.So the density can be changed into the area that can be defined and measured easily.We may know where is denser and where should be simplified firstly.But one thing must be pointed out here that every point is regarded as nonobjective,and they divide the space on the equal principle.It is the basis and accords with the Voronoi principle. Thirdly,the distribution center can be extracted from gray image.The new concept and methods mentioned above are integrated into a recognition and measurement model for spatial distribution properties of point cluster.It becomes the basic of point cluster generalization. Finally,a generalization model of point cluster is provided in this paper on the basis of Voronoi diagram establishment in a dynamic way.According to the principle,an iterative method is proposed.Through different extractive times,different scale span results can be obtained. In a word,the generalization model,which is established in this paper,preserves the spatial distribution properties of point cluster very well.And all the methods mentioned above have been proved to be feasible and sound.

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