计算机地形分析中的多边形迭加问题

在计算机地形分析中,研究地形多要素迭加分析的关键是解决多边形的迭加问题。本文通过对多边形迭加的数学方法的研究,提出了适合计算机多要素分析的原理和方法。     

An equal area conversion model for rasterization of vector polygons

Vector-raster conversion is one of the classic research topics in the field of Geographical Information Systems (GIS). The algorithms commonly used in GIS are devoted to maintaining the vector polygons' shape characteristics, but neglect the gain and loss of a polygon's area, which is another important attribute. This paper proposes an equal-area conversion model based on an area compensation optimization principle. According to the topological relationship among polygons and boundary grids, a neighborhood compensation principle was adopted to assign the attributes of boundary grids and a global optimization algorithm was developed to minimize area distortion in the whole data set. Two experiments were designed and the results indicated that this algorithm not only guaranteed the area error is as small as possible, but also has the advantage as being adaptive to polygon shape and spatial structure.     

三维相干切片断层多边形检测

作者在本文中将三维切片视为二维图像，利用图像边缘检测技术，检测出相干切片上断层多边形，并将其直接投影到测网底图上，用于构造成图。该项技术不仅提高了用相干切片解释断层的精度，而且提高了断层解释效率，使相干数据体技术在三维解释中的作用得到更大的发挥。实际应用表明，三维相干切片断层多边形检测技术，是全三维高精度解释的有力工具，提高了解释效率，缩短了解释周期。     

软多边形地图要素弯曲识别模型及其应用研究

以边界线构减点的坐标单调性变化分析为基础，提出了地图上软多边形要素弯曲识别模型，给定了弯曲的凸凹性定义，建立了弯曲的多种形态量化指标，规范了弯曲邻接关系的形式及确定方法。研究了模型在软多边形地图要素图形综合简化中的应用，形成了简化这类要素图形的三种基本手段。     

中国地区SRTM3 DEM高程精度质量评价

为揭示我国SRTM3DEM数据高程精度质量,结合已开展过SRTM3DEM高程精度质量评价工作的局部地区的研究,考虑空间分布情况,选取新疆、辽宁、山东、浙江、海南5个地区的平原、丘陵、盆地、山地等地形区域作为典型研究区,并以1∶5万DEM为假定真值、以1∶25万DEM为参照,通过DEM面误差可视化分析、DEM面误差信息熵模型、中误差模型等方法对SRTM3DEM数据高程精度质量做了分析。计算结果表明我国SRTM3DEM数据高程精度质量受地形影响并存在一定的空间差异性,同时我国范围内SRTM3DEM数据高程精度质量整体上要高于1∶25万DEM。     

基于拓扑构建的多边形区域间运算的新方法

提出了一种新的直观的方法进行多边形区域之间的运算.首先将需要计算的多边形区域的边进行自动拓扑构建,利用多边形区域的边将平面划分为n个小多边形区域;然后生成这些多边形区域的内点,通过判断小多边形区域的内点是否在原始多边形区域内来确定小多边形区域是否选取;最后合并选取的小多边形即为所求.试验结果表明,该方法思路清晰、鲁棒性强,在GIS中得到了有效的运用.     

多边形自动生成的一种改进算法

本文提出了一种多边形自动生成的改进算法,对不参与组成多边形的弧段和结点进行分类处理,在搜索多边形之前,排除悬挂结点、悬挂弧段、假悬挂结点和假悬挂弧段的干扰,在搜索多边形的过程中,为弧段建立搜索标志,并对桥进行判断和排除,较好地解决了问题,提高了自动生成多边形的效率。     

策克台井下宽频带地震计GL?S120B正弦波标定衰减因子的讨论

为解决EDAS?24GN6数据采集器对策克台井下宽频带地震计GL?S120B进行正弦标定的过程中产生的响应波形限幅问题,分析了数据采集器?地震计这一系统的标定过程及原理,结合GL?S120B的幅频响应特征,推导出正弦标定频点参数中的频率f、衰减因子λ与标定响应波形限幅之间的关系。分析表明,在GL?S120B平坦响应频带内,为避免出现标定波形响应限幅,频率f与衰减因子λ的乘积应至少大于限幅下限50.93;而在其衰减响应频带内,由于地震计电压灵敏度的衰减,频率f与衰减因子λ的乘积最小取值也相应减小。据此,对衰减因子λ进行校正,并再次对GL?S120B进行正弦标定。标定及计算结果显示:标定波形未限幅,GL?S120B三分向幅频响应特征较为一致;灵敏度对出厂值的变化率保持在5%范围内。本研究结论可为采用EDAS?24GN6和GL?S120B地震观测系统的台站进行正弦波标定提供频点参数,并为其他台站解决正弦标定波形限幅问题提供分析依据。     

GIS中矢量多边形网格化问题研究

在实际的GIS空间分析过程中,为了更加简便快捷的实现某些特定的空间分析功能,常常需要将不规则的矢量多边形区域转化为规则的格网区域。该文介绍了矢量多边形网格化的四类算法：中心点归属法、面积占优法、重要性法和面积内插法。其中面积内插法又可分为面积权重内插法、基于表面模型的面积内插法和基于统计模型的面积内插法。同时介绍了各类算法的实现思想,并比较了其优劣及应用范围,认为基于表面模型的面积内插法是一种比较理想且极具发展前景的矢量多边形网格化方法。     

A comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings

ABSTRACT

The aim of this article is to describe a convenient but robust method for defining neighbourhood relations among buildings based on ordinary Delaunay diagrams (ODDs) and area Delaunay diagrams (ADDs). ODDs and ADDs are defined as a set of edges connecting the generators of adjacent ordinary Voronoi cells (points representing centroids of building polygons) and a set of edges connecting two centroids of building polygons, which are the generators of adjacent area Voronoi cells, respectively. Although ADDs are more robust than ODDs, computation time of ODDs is shorter than that of ADDs (the order of their computation time complexity is O(nlogn)). If ODDs can approximate ADDs with a certain degree of accuracy, the former can be used as an alternative. Therefore, we computed the ratio of the number of ADD edges to that of ODD edges overlapping ADDs at building and regional scales. The results indicate that: (1) for approximately 60% of all buildings, ODDs can exactly overlap ADDs with extra ODD edges; (2) at a regional scale, ODDs can overlap approximately 90% of ADDs with 10% extra ODD edges; and (3) focusing on judging errors, although ADDs are more accurate than ODDs, the difference is only approximately 1%.