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面向带洞面状对象间的拓扑关系描述模型 总被引：1，自引：1，他引：0
为研究带洞面状对象间的拓扑关系,提出了一种25IM(25交集模型)。以点集拓扑理论为基础,对带洞面状区域的内部、边界和外部进行定义。分析了9IM(9交集模型)在表达带洞面状对象间拓扑关系方面存在的问题,将带洞面状对象分为内部、外边界、内边界、外边界外部、内边界外部共5部分,提出了一种5×5的矩阵模型,即25IM。基于点集拓扑理论,定义了8条规则来排除不符合逻辑的拓扑关系。基于25IM,对8种基本拓扑关系:相离、相接、重叠、覆盖、包含、相等、被覆盖和被包含,进行细分描述。结果表明,本文提出的25IM能够更为详细地表达带洞面状对象间的拓扑关系。 相似文献
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Representing the topological relations between directed spatial objects has gained increasing attention in recent years. Although topological relations between directed lines and other types of spatial objects, such as regions and bodies, have been widely investigated, few studies have focused on the topological relations between directed lines and directed regions. This research focuses on the representation and application of directed line–directed region (DLDR) topological relations, and may contribute to spatial querying and spatial analyses related to directed spatial objects or time‐varying objects. Compared with other topological relation models, a DLDR model that considers the starting and ending points of the directed line and the front and back faces of directed regions is proposed in this research to describe the topological relations between directed lines and directed regions. DLDR topological relations are presented, the completeness of the 111 DLDR topological relations is proved, and the topological relations based on the 9‐intersection model (9IM), 9+‐intersection model (9+‐IM), and DLDR model are compared. The formalism of the DLDR model and the corresponding geometric interpretations of the 111 DLDR topological relations are presented, seven propositions are stated to prove the completeness of the 111 DLDR topological relations, and the case study shows that more detailed topological relation information can be obtained based on the DLDR model. 相似文献
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Classification of topological relations between spatial objects in two‐dimensional space within the dimensionally extended 9‐intersection model 
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下载免费PDF全文 As an important topological relation model, the dimensionally extended 9‐intersection model (DE‐9IM) has been widely used as a basis for standards of queries in spatial databases. However, the negative conditions for the specification of the topological relations within the DE‐9IM have not been studied. The specification of the topological relations is closely related to the definition of the spatial objects and the topological relation models. The interior, boundary, and exterior of the spatial objects, including the point, line, and region, are defined. Within the framework of the DE‐9IM, 43 negative conditions are proposed to eliminate impossible topological relations. Configurations of region/region, region/line, line/line, region/point, line/point, and point/point relations are drawn. The mutual exclusion of the negative conditions is discussed, and the topological relations within the framework of 9IM and DE‐9IM are compared. The results show that: (1) impossible topological relations between spatial objects can be eliminated by the application of 43 negative conditions; and (2) 12 relations between two regions, 31 relations between a region and a line, 47 relations between two lines, three relations between a region and a point, three relations between a line and a point, and two relations between two points can be distinguished by the DE‐9IM. 相似文献
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空间拓扑关系描述是空间关系的主要内容,是与人类的认知概念一致的,在空间数据查询与挖掘等方面有重要应用。很多学者研究了不带空洞的面对象间的拓扑关系,但对于带空洞的面对象间的拓扑关系研究甚少。首先回顾了现有模型,并指出了各模型的优缺点,然后根据简单面对象的8种基本空间拓扑关系,对带多个空洞的面对象的拓扑关系进行了层次组合分析,提出了一种能描述带多个空洞的复杂面对象间的拓扑关系的层次组合模型。该模型能描述带多个空洞的复杂面对象间的所有拓扑关系,而且不因面对象中空洞的编号顺序不同导致模型所描述的结果不同,同时也弥补了4-4ID模型只能描述带一个空洞的面对象的不足。 相似文献
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三维体目标间拓扑关系与方向关系的混合推理 总被引：1，自引：1，他引：0
重点研究了三维空间中拓扑关系和方向关系间的混合空间关系推理。用Allen区间关系对描述基于投影的空间划分方法得到的方向区域和用九交矩阵描述的拓扑关系,用定义法研究混合空间关系推理,推理结果用组合推理表表示。 相似文献
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目前研究已提出了多种带洞面域拓扑关系的描述模型,建立不同模型之间的联系可发挥这些模型在拓扑关系推导和分析中的优势。本文对比分析了基于点集拓扑和对象分解两种方法的6种拓扑关系描述模型,通过定义两个25交关系矩阵操作算子,建立整体面域与分解区域间的拓扑关系计算方法,实现了拓扑关系描述模型之间的转换。理论证明,表明关系矩阵表和扩展9交集模型,以及4元组模型与25交模型在表达拓扑关系的能力方面是一致的且可以相互转换,关系矩阵表可转换为25交模型和9交模型。实例分析说明本文方法可以利用25交模型的“桥梁”作用实现多种模型之间的转换,描述具有特定结构带洞面域间的拓扑关系。 相似文献
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针对原有方向关系矩阵模型对于参考目标MBR区域的方向描述缺陷问题,本文将拓扑约束引入方向关系定性描述,构建基于拓扑参考的方向关系定性描述模型,实现了MBR区域方向关系的有效表达。新模型首先将参考目标的MBR区域划分为不同的拓扑区域,提出方向关系拓扑参考定义;基于拓扑参考,分别对不同拓扑区域定义相应的方向关系矩阵;最后,根据参考目标与源目标间的不同拓扑关系,提出不同情况下方向关系分层定性描述策略。实验结果表明,新模型充分反映了拓扑关系对方向关系描述的约束关系,能有效提高方向关系表达的准确性和精确性。 相似文献
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空间查询中自然语言空间关系与度量空间关系的转换方法研究:以面目标为例 总被引：1，自引：0，他引：1
以面目标为例,首先探讨度量空间关系的描述方法,即拓扑谓词+度量参数,建立拓扑谓词与度量参数之间的对应关系;然后从人的认知角度,分析空间关系的自然语言描述,即拓扑谓词+量词,区分模糊量词和临界量词两类量词,并分别用来描述邻近拓扑谓词相互转化过程中的定量变化和定性变化;最后,分析度量参数与量词之间的关联关系,试图建立空间查询中度量空间关系与自然语言空间关系之间的联系,从而更好地应用于空间查询.Abstract： Spatial relations between spatial entities can be obtained by two means. One is computed and obtained by spatial data, and the other is obtained by human spatial cognition. So, they are named as metric spatial relations and natural-language spatial relations, respectively. This paper is focused on filling the gap between these two kinds of spatial relations. For this purpose, regions are taken as example, and three steps are included. Metric spatial relations ore first depicted by combining topological predicates with metric parameters, where a mapping is built between the topological predicates and the metric parameters. Second, natural-language spatial relations are represented by combining topological predicates with quantifiers, where quantifiers are differentiated into two types:vague quantifiers and critical quantifiers. They are respectively used to describe quantitative and qualitative change in the process of the transformation between neighboring topological predicates. Finally, the association between metric parameters and quantifiers is further illustrated and analyzed by a questionnaire. With these three steps, the linkage can be built between metric spatial relations and natural-language spatial relations. The proposed methods have been proven to be rational by the performances of spatial query. 相似文献
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A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis 总被引：2，自引：0，他引：2
Yu Liu Qinghua Guo Maggi Kelly 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):461-475
Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. 相似文献
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在图形简化中面状目标间拓扑关系渐进式转换的研究 总被引：1，自引：0，他引：1
拓扑关系是空间关系中最为重要的关系,在空间抽象中,拓扑关系会发生变化,如何控制这种变化?如何在更抽象的层次上维护空间拓扑关系的一致性,就是一个非常重要的问题。本文以拓扑关系成分抽象的转换方法为基础,研究了面之间拓扑关系的抽象规则,并详细探讨了这些拓扑关系的渐进式转换方法,绘制了相应的面之间基本拓扑关系渐进式转换图。 相似文献
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Eliseo Clementini Brahim Lejdel Sabrina Mazzagufo Robert Laurini 《Transactions in GIS》2021,25(1):491-515
In GIS, spatial analysis is based on the use of spatial operations such as testing the spatial relations between features. Often, such tests are invalidated by errors in datasets. It is a very common experience that two bordering regions which should obey the topological relation “meet” fall instead in the “overlap” category. The situation is exacerbated when applying topological operators to regions that come from different datasets, where resolution and error sources are different. Despite the problem being quite common, up to now no standard approach has been defined to deal with spatial relations affected by errors of various origins. Referring to topological relations, we define a model to extend the eight Egenhofer relations between two simple regions: we call them homological relations (H‐relations). We discuss how exact topological relations can be extracted from observed relations and discuss the case of irregular tessellations, where errors have the most impact on vector data. In the proposed case study within the domain of geographic crowdsourced data, we propose algorithms for identifying homological regions and obtaining a corrected tessellation. This methodology can be considered as a step for quality control and the certification of irregular tessellations. 相似文献
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带空洞面对象间拓扑关系形式化描述 总被引：1，自引：0，他引：1
利用Egenhofer等提出的含空洞的两面对象间空间拓扑关系描述框架,在四交差模型的基础上,提出了一种能描述带空洞复杂面对象间的空间拓扑关系的扩展模型——4-4ID模型,并用该模型详细推导了简单面对象和仅带一个空洞面对象间以及两个仅带一个空洞面对象间有意义的拓扑关系。 相似文献
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球面四元三角网的基本拓扑关系描述和计算 总被引：6，自引：1，他引：5
球面四元三角网具有多分辨率和层次组织的特性,已成为目前研究球面问题的有效方法之一。本文在此基础上,利用引入集合多算子和对称差的欧拉数,给出描述和计算球面栅格拓扑关系的四元组模型。该模型利用两空间目标间的交(∩)、差(\)、被差(/)和对称差(Δ)的内容是否为空来初步区分相离/相接、交叉、相等、包含/覆盖、被包含/被覆盖这五对拓扑关系。然后通过引入对称差的欧拉数来进一步区分传统模型难以区分的相离/相接、包含/覆盖和被包含/被覆盖这三对拓扑关系。 相似文献
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Zhaoyuan Yu Wen Luo Linwang Yuan Yong Hu A‐xing Zhu Guonian Lü 《Transactions in GIS》2016,20(2):259-279
Classical topological relation expressions and computations are primarily based on abstract algebra. In this article, the representation and computation of geometry‐oriented topological relations (GOTR) are developed. GOTR is the integration of geometry and topology. The geometries are represented by blades, which contain both algebraic expressions and construction structures of the geometries in the conformal geometric algebra space. With the meet, inner, and outer products, two topology operators, the MeetOp and BoundOp operators, are developed to reveal the disjoint/intersection and inside/on‐surface/outside relations, respectively. A theoretical framework is then formulated to compute the topological relations between any pair of elementary geometries using the two operators. A multidimensional, unified and geometry‐oriented algorithm is developed to compute topological relations between geometries. With this framework, the internal results of the topological relations computation are geometries. The topological relations can be illustrated with clear geometric meanings; at the same time, it can also be modified and updated parametrically. Case studies evaluating the topological relations between 3D objects are performed. The result suggests that our model can express and compute the topological relations between objects in a symbolic and geometry‐oriented way. The method can also support topological relation series computation between objects with location or shape changes. 相似文献
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反转运算作为空间方向关系最为基础的一种运算,目前对其研究还没有达到形式化和系统化的要求。这里以空间方向关系的集合表示为基础,在严格定义方向关系的前提下,从点状目标间方向关系的反转运算出发,分类研究了各种矩形主方向关系的反转运算规则,对两目标出现重合区域的方向关系进行了重点研究,给出了运算结果并予以证明。 相似文献
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线与线之间的空间拓扑关系组合推理 总被引：4，自引：0，他引：4
在空间拓扑关系组合描述的基础上，进一步完善了利用基本空间拓扑关系进行组合推理的方法。并建立了组合表。详细绘制了两条线之间的空间拓扑关系图。 相似文献
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To design retrieval algorithm of spatial relations for spatial objects with randomness in GIS, this paper builds up the membership functions based on set theory idea, used for determination of topological spatial relations between random objects, such as between point and point, point and line or polygon, which provides theoretical basis for retrieving spatial relations between certain and random objects. Finally, this paper interprets detailed methods and steps of realizing them by means of some simple examples under the GIS's environment. 相似文献