海图自动综合研究进展

海图自动综合研究由传统地图综合研究和海部要素综合算法两部分组成,是海图生产自动化的瓶颈之一。简要分析了海图海部要素综合算法的特点,思考和展望了海图综合的未来发展方向:深入研究海部要素的特征,重视基于三维模型等全局性综合算法,加强海图自动综合的系统化和工程化,以及海洋信息多尺度表达的研究。     

数字海图水深的自动综合探讨

海图上的水深具有以点代面的特殊性质,不仅是影响载负量的关键,而且水深点的不同形态和密度又是海底地形特征的反映。因而,只有经过海底地形特征的识别,同时参照海底地形的综合原则,才能实现水深综合的自动化。     

数字海图的自动综合方法研究

作为现代地图学的重要组成部分,自动综合问题一直是影响地图学发展的重要问题。在总结前人研究成果和现有的空间信息无级比例尺自动综合方法的基础上,以面向地理特征为出发点,以矢量化的电子海图数据为信息源,提出了比较实用的在数字海图分层支持下的自动综合方法。以海图中的部分要素层为例进行分析,并给出了具体的自动综合流程。     

数字海图中助航设备的自动综合探讨

助航设备是海图上一种重要的点状要素,其自动综合是海图自动综合的重要研究内容.以数字海图数据为基础,深入探讨了数字海图中助航设备的自动综合问题,重点研究了助航设备的自动选取.基于助航设备分布和分级的规律性,提出了一种助航设备自动综合方法.初步实验表明,该方法在实现助航设备自动综合时效果较好.     

海图水深注记自动综合研究

本文通过对Delaunay三角网的利用和对TIN(Triangulated Irregular Network不规则地形三角网)的化简方法实现了对海图中特征水深注记的选取;并在此基础上建立了具有水深注记分区选取、人机交互选取水深注记、菱型网设置、三角网检测等多项功能的海图水深注记自动综合子系统。     

基于ArcInfo的海图等高线自动制图综合研究

等高线制图综合是海图生产中工作量比重较大的一项工作,通常采用人工处理的方式,效率较低,影响成图速度。根据海图等高线制图综合特点,利用ArcInfo系统相关功能,研究提出了一套相对较好的等高线自动制图综合方法,并在实际作图过程中得到了较好的应用,提高了海图生产效率。     

数字海图中海洋陆地层自动校对检测研究

数字海图生产过程中,海洋陆地层面要素和线要素的校对检测是海图校对检测的重点与核心。通过对数字海图海洋陆地层面要素、线要素和水深层数据的综合分析研究,按照航海图书编绘规范要求,基于ArcInfo地理信息系统软件平台,应用AML宏语言,主要解决了面要素的编码属性自动识别、识别结果与作业结果自动比对、面要素综合错误自动检测和多余面要素自动检测等关键问题,实现了海洋陆地层线要素与面要素的自动校对检测。     

基于曲线特征的等深线自动综合方法研究

从等深线的特点及其制图综合的基本原则与方法出发,提出了一种基于曲线特征的Douglas-Peucker改进算法,在此基础上构建了等深线自动综合方法。该方法通过对曲线特征点的提取和弯曲特征的分析,可以自动确定等深线综合的数量指标和最小分界指标,经试验验证取得了较好的综合效果。     

电子海图最优航线自动生成算法的改进

从航路二叉树的构建、可操作性质量评价指标的构建以及最短距离航线算法与最短时间航线算法相结合三方面,初步探讨了航线自动生成的改进算法,以得到更合理和实用的航线。     

Automatic Sounding Generalization in Nautical Chart Considering Bathymetry Complexity Variations

Sounding acts as the main feature in a digital nautical chart as it describes the concerned marine topography for the safety of navigation. Unlike the geometry-oriented selection of point feature, the generalization of soundings for chart compiling is expected to be context-oriented, which means bathymetry complexity variations across the study region should be preserved in the sounding selection process. However, such variations are not explicitly accessible to automated systems. This paper proposes an approach that effectively analyzes and measures bathymetry complexity from sounding data, with a focus on topography variations among different regions. The presented approach first divides the exploring region into several subregions, by adopting techniques of computational geometry and graph theory. Then, the approach quantitatively measures the bathymetry complexity of the subregions from grid-based digital terrain model. Finally, a composite bathymetry complexity index integrating aspects of steepness and depth variation is developed to guide the operation of sounding selection in different subregions. Generally, when seafloor is rugged with steep slopes, the number of soundings is high. While in flatter areas, a smaller amount of soundings is retained. The potential of our approach is demonstrated by an application to a real data set.     

RSG数字高程模型的自动综合研究

数字高程模型(DEM)作为对地表的数字描述,已逐渐成为国家空间信息基础设施的重要组成部分。探讨了对RSG结构DEM实施自动综合的需求,认为对规则方形格网(RSG)结构DEM综合必须基于合理的存储结构。提出的基于线性四叉树的综合模型能很好的兼顾RSG的结构保留和冗余消除。     

矢量海图通告改正自动处理方法研究

在分析了国内外矢量海图通告改正技术现状的基础上,重点研究了实现矢量海图通告改正所必需的通告符号与海图要素自动转换、改正位置自动计算、改正要素自动匹配等自动处理方法,并举例说明了矢量海图通告改正的自动处理过程。     

Recognition and Measurement of Marine Topography for Sounding Generalization in Digital Nautical Chart

Generalization is a comprehensive process. It is not simply a question of an algorithm, such as simplification, selection, displacement, and so on. Only after geometric shapes and topological properties have been understood fully, can a sound and automated generalization process be possible. This article proposes a new theoretical model for sounding generalization in digital nautical charts. First, with the aid of Delaunay triangulation, a tree structure is introduced for a hierarchical representation of the marine topography. Then, an analytical algorithm for the recognition and the measurement of the marine topography is developed through the use of the tree structure. Finally, all of the techniques mentioned above are integrated into a model for sounding generalization, of which results are illustrated with the aid of several examples.     

海图上点群状特征的识别与量测

点群状（包括图斑群）目标在GIS数据中有大量的实例,由于它不同于单个独立的点状要素,在海图自动综合中如何对其分布特性进行识别并加以定量描述,是进行点群综合过程中的关键问题。主要介绍如何利用Delaunay三角网与Voronoi图两种模型对点群状目标分布特性进行描述,并通过对Delaunay三角网与Voronoi图的“蚕食”以及引入图像处理概念将密度问题转化为图像处理问题的方法,直接或间接对点群状目标进行识别与量测。     

Recognition and Measurement of Marine Topography for Sounding Generalization in Digital Nautical Chart

Generalization is a comprehensive process. It is not simply a question of an algorithm, such as simplification, selection, displacement, and so on. Only after geometric shapes and topological properties have been understood fully, can a sound and automated generalization process be possible. This article proposes a new theoretical model for sounding generalization in digital nautical charts. First, with the aid of Delaunay triangulation, a tree structure is introduced for a hierarchical representation of the marine topography. Then, an analytical algorithm for the recognition and the measurement of the marine topography is developed through the use of the tree structure. Finally, all of the techniques mentioned above are integrated into a model for sounding generalization, of which results are illustrated with the aid of several examples.

     

海图上岛屿自动选取的一种模型

岛屿的自动选取是实现岛屿自动综合的前提，在分析岛屿选取特点的基础上，给出了岛屿自动选取模型。该模型采用了系统聚类和Vironoi图两项关键技术，在整体选取的基础上实施局部控制，使得岛屿选取更加趋向客观实际。