海底地形的识别与水深的综合

水深综合是数字海图综合的重要方面,也是其难点之一.在分析水深综合原则的基础上,提出了基于地形特征识别的综合模型,以分解其诸多约束条件,来实现水深的自动综合.     

面向海图综合的复杂海底地形区域识别

复杂海底地形区域识别是实现海图自动制图综合的必要环节。从服务海图水深注记选取的角度,提出一种面向海图制图综合的复杂海底地形区域自动识别方法。首先,对复杂海底地形的概念和主要特征进行了分析,据此提出了对应的地形特征量化参数;其次,通过对每个水深注记地形特征的度量,筛选出复杂地形特征水深注记;最后,利用聚类技术对提取的复杂地形特征水深注记进行群组划分,并提取出每个群组的边界多边形,从而得到每块复杂海底地形区域的范围。实验表明,各种复杂海底地形区域都能被有效识别,且识别结果与人工识别结果基本一致。     

中心内缩综合元的水深自动综合模型

针对水深自动综合问题,该文在满足水深综合的特殊要求并保持综合前后水深分布空间一致性的基础上,利用Voronoi图工具,采用定性和定量分析方法,建立了相应的综合规则和控制流程,设计了一种中心内缩综合元的水深自动综合模型,并进行了实验验证。实验结果表明,该模型可以在保证水深综合质量的前提下,实现流程的自动化。     

海图水深综合的人工神经元网络方法

水深或水深点是海图的重要内容,水深综合是海图制图综合的重要方面,也是海科综合的难题之一。本语文在分析海图水深化综合要求的基础上,研究了水深综合的神经元网络的网络结构与作业策略、网络参数、网络学习、网络实现,并给出了实验结果。     

多种遥感数据的综合编图研究

分析了多光谱、卫星测高和成像雷达3种数据的几何定位精度、水深反演有效范围和灵敏度等特性,利用TM、SPOT和SAR数据,研究其水深反演精度和同一性,由此确定了水深综合编图的原则。采用加权平均的数据融合技术,探讨了编制理想综合图件的方法。     

星载SAR水下地形和水深遥感的最佳雷达系统参数模拟

根据星载合成孔径雷达 (SAR)浅海水下地形和水深成像机理 ,建立了浅海水下地形和水深雷达后向散射截面仿真模型。该模型包括奈维 斯托克斯方程、谱作用量平衡方程和雷达后向散射模式。利用该模型仿真结果 ,探讨了不同波段 (P、L、C和X)、不同极化 (VV和HH)和不同入射角 (2 0°— 70°)的星载SAR测量浅海水下地形和水深的能力。研究结果表明 ,浅海水下地形和水深遥感的最佳波段为P波段 ,L波段次之 ,C波段比X波段要好一些。VV极化SAR的测量能力要强于HH极化。 2 0°— 40°是星载SAR测量浅海水下地形和水深的最佳入射角范围。     

数字海图水深注记的自动综合研究

以海图中水深综合的基本原则为依据,模拟人在制图综合时的思维方式,提出一种新的水深注记自动综合的模式--表达、识别与综合,并通过试验验证该综合模式的实用性和有效性,为自动制图综合的研究开创新的理论与研究方法.     

基于菱形计盒分维估值计算的水深综合方法

文中提出一种基于菱形计盒分维估值计算的二倍数水深综合方法,对水深点群进行以菱形尺度单元进行分形计算,验证可行性;利用分维值确定目标比例尺下单位尺度内的压缩比,从而确定保留点数。该方法可以应用于快速确定综合目标点数,提高水深选取效率,且构建的菱形网可作为水深综合的参考网。     

基于约束条件的不规则水深网构建方法

针对传统水深综合原则适用性不强、可操作性差等缺陷,利用海底地形骨架线作为反映区域趋势的趋势线。构网过程中,通过约束角度和距离控制网络结点的生成,实现水深网的自动构建。实验证明,该方法可以很好地控制水深分布密度和结构,反映出的海底地形趋势比传统菱形网更加合理,为水深自动综合提供了一个新思路,具有一定的可行性和实用价值。     

规则格网DEM地形综合方法分析

针对当前丰富多样的DEM地形综合模型,分类阐述其综合原理、关键技术及优缺点,以黄土丘陵1：1万DEM为基础,对其地形综合效果进行对比分析。研究结果表明：现有的DEM地形综合模型各有优缺点;不同综合模型综合后的地形,高程数值随着综合尺度的增加,存在显著差异,而且不同综合模型对流域边界的综合处理也存在显著差异,没有形成客观有效的流域边界描述;当前困扰DEM地形综合的关键问题,是缺乏不同尺度下地形综合质量的客观评价标准,致使现有综合模型地形综合结果和DEM格网分辨率之间没有建立本应存在的内在联系,有待进一步深入研究。     

An ontology-driven multi-agent system for nautical chart generalization

ABSTRACT

On nautical charts, undersea features are portrayed by sets of soundings (depth points) and isobaths (depth contours) from which map readers can interpret undersea features. Different techniques were developed for automatic sounding selection and isobath generalization. These methods are mainly used to generate a new chart from the bathymetric database or from a larger scale chart through selection and simplification. However, a part of the process consists in selecting and emphasizing undersea features formed by groups of soundings and isobaths on the chart according to their relevance to maritime navigation. Hence, automation of the process requires classification of features and their generalization through the application of a set of operators according not only to geometric constraints but also to their meaning.

The objective of this work is to conceive a multi-agent system (MAS) for nautical chart generalization that is driven by the knowledge on the generalization process and the undersea features and their relationships. First, this work provides a feature-centered ontology modeling of the generalization process. Then, the MAS structure is introduced where agents access cartographic knowledge stored in the ontology. The MAS makes use of measure algorithms to evaluate constraint violations on the chart in order to decide which generalization operators to apply. The whole model has been implemented to provide generalization plans on a real case study.     

An Ontology of the Submarine Relief for Analysis and Representation on Nautical Charts

Abstract

A nautical chart is a kind of map used to describe the seafloor morphology and shoreline of adjacent lands. One of its main purposes is to guarantee safety of navigation. As a consequence, the construction of a nautical chart follows very specific rules. The cartographer has to select and highlight undersea features according to their relevance to navigation. In an automated process, the system must be able to identify and classify these features from the terrain model. This paper aims therefore to define ontologies of the submarine relief and nautical chart that will be at the root of a model-oriented generalization process. To the best of our knowledge, no ontology has been defined to formalize the geographical and cartographic objects for nautical chart representation. Thus, a bottom-up approach was developed to extract and model knowledge derived from standards established by the International Hydrographic Organization (IHO) and cartographers’ expertise. The submarine relief ontology formalizes undersea features describing the submarine relief. Four concepts (composition, morphometric class, shape value and depth value) are introduced to describe properties and relationships between undersea features. The cartographic representation ontology of nautical charts will define several concepts (chart, features, isobathymetric lines and soundings) for the representation of undersea features on the chart.     

An algorithm for automatic cartographic sounding selection

Soundings on nautical charts provide information about the shape of ocean bottom between chart depth curves. A single chart may have thousands of soundings posted on it. This paper describes a new algorithm for selecting soundings automatically. At first,the problem encountered in automatic cartographic sounding selection is presented. Then the authors give the principle of the new algorithm,and the implementation is illuminated in detail. At last the experiments prove the rationality and efficiency of the new algorithm.     

An algorithm for automatic cartographic sounding selection

Soundings on nautical charts provide information about the shape of ocean bottom between chart depth curves. A single chart may have thousands of soundings posted on it. This paper describes a new algorithm for selecting soundings automatically. At first, the problem encountered in automatic cartographic sounding selection is presented. Then the authors give the principle of the new algorithm, and the implementation is illuminated in detail. At last the experiments prove the rationality and efficiency of the new algorithm.     

海底地貌数据综合研究进展

海底地貌数据与人类认知海洋、探索海洋、利用海洋息息相关,海底地貌数据综合是海图编制、海洋空间数据处理的重点研究内容之一。海底地貌数据综合属于地图综合的研究范畴,但受历史渊源、学科体系、应用范畴等诸多因素影响,常被认为是地图综合中相对独立、特殊的研究内容。本文在明确海底地貌数据综合特殊性的基础上,从数据增强、综合方法、质量评价3个方面梳理海底地貌数据综合研究进展,归纳了几种常见的海底地貌数据综合应用案例,并从研究内容、技术方法、实践应用3个方面探讨了海底地貌数据综合的发展趋势。     

Quality Assessment of an Automatic Sounding Selection Process for Navigational Charts

A good seabed representation is one of the important characteristics of any navigational chart. Along with depth contours and coloured depth areas, soundings are used for this task. All the soundings on a navigational chart are selected for a reason. Soundings contribute to the navigational chart safety aspect by alerting to all the threats and dangers. They also show all the attributes of a seabed relief without overcrowding it, thus maintaining the overall chart quality. Soundings are selected from a hydrographic survey and since it consists of a vast number of data, the process of sounding selection is a challenging and demanding task. It requires experience and knowledge from the nautical cartographer and is mostly done manually. Some types of software nowadays provide an automatic selection feature. This paper analyses a process of automatic sounding selection in the dKart Editor software. On the Croatian side of the Adriatic Sea, ?ibenski Kanal (?ibenik channel) and Kanal Sv. Ante (St. Ante’s channel) are used as the study area. A hydrographic survey of the area represents the input data. The official navigational chart of the surveyed area is used as the basis for determining three different sets of parameters for the selection process. After the selection, obtained results are assessed based on geometrical accuracy and on the conservation level of navigational safety. For geometrical accuracy, the best results were produced by the third set that was divided in two subsets for each channel. It was determined that the nature of the seabed relief had an impact on the selection process. The same set had the best result for navigational safety assessment but it was concluded that all the sets undermined the aspect. Because of these crucial shortcomings noticed in all the tested sets of parameters, the feature is considered inadequate for serious usage as a completely automatic tool for the process of sounding selection on navigational charts.     

Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach

A nautical chart provides a schematic view of the seafloor where isobaths (contour lines joining points of same depth) and depth soundings are generalized to highlight undersea features that form navigational hazards and routes. Considering that the process is ultimately driven by features and their significance to navigation, this article proposes a generalization strategy where isobath generalization is controlled by undersea features directly. The seafloor is not perceived as a continuous depth field but as a set of discrete features composed by groups of isobaths. In this article, generalization constraints and operators are defined at feature level and composed of constraints and operators applying to isobaths. In order to automate the process, a multi‐agent system is designed where features are autonomous agents evaluating their environment in order to trigger operations. Interactions between agents are described and an example on a bathymetric database excerpt illustrates the feasibility of the approach.     

顾及局部差异的航海图线要素复杂度度量EI北大核心CSCD

针对当前线要素复杂度的度量多基于单一线要素求取整体复杂程度,难以顾及局部差异变化这一现状,提出一种用于航海图线要素复杂程度的度量方法。首先,分析了线要素节点所构三角网边长与线要素复杂程度的关联性;然后,通过构建约束Delaunay三角网、计算线要素数据点复杂度并内插等操作,得到了航海图线要素复杂场;最后,选取两组不同的典型海域航海图作为试验数据,并通过统计试验验证了本文方法的合理性。试验结果表明:本文方法在顾及局部差异的基础上,能较好地定义航海图线要素的复杂度,并可精细化地定量评估航海图线要素在各区域的复杂程度。     

数字海图线性特征的识别、量测与综合

图形特征的变化是无究的,只是依据与位移、夸大、化简等相似的综合方法,而不包含图形特征的识别与量测,数字海图的自动综合是无法实现的。只有识别、量测和综合方法的组合,才是数字海图综合概念的全部体现。因而,本文模拟人的综合方法的同时, 点模拟了人的图形特征的识别方式,同时,经过Ｄｏｕｇｌａｓ二叉树方法的引入,给出了图形特征的识别与量测函数,实现了数字海图红性特征的自动综合。