不确定区域间拓扑关系定性推理

提出不确定区域间拓扑关系的组合描述法,它对拓扑关系的描述能力与扩展九交完全相同.但组合描述模型的优点在于用已有的5种面/面基本拓扑关系的组合来表示44种不确定区域间的复杂拓扑关系,可以把44种复杂拓扑关系的推理转化为5种基本拓扑关系推理的组合,从而实现不确定区域间拓扑关系的推理.     

不确定线-面拓扑关系的描述与判别

不确定线状目标和面状目标之间拓扑关系的描述是空间信息处理过程中经常面对的问题。在对不确定线状目标和面状目标进行描述的基础上,对不确定线状目标与面状目标各组成部分之间的相交程度进行了定量表达,通过计算这些度量组成的空间向量与9-交集模型确定的空间关系向量之间的相关度,提出了一种不确定线状目标和面状目标之间拓扑关系的描述模型,通过定量的方法来对其空间拓扑关系进行判别。     

“蛋-黄”模型的拓展研究

通过研究不确定数据间的拓扑关系,针对"蛋-黄"模型中46个拓扑关系内部逻辑关系不清的缺点,给出了"蛋-黄"模型的邻近拓扑关系之间的动态变化图和相应的93个拓扑关系,去掉41个含"切"的拓扑关系和6个重复的拓扑关系,仍然得到46个拓扑关系,这与Cohn的理论是一致的,在完善了"蛋-黄"模型的同时提高了其空间推理能力。     

不确定拓扑关系模糊推理

针对定性空间推理难以处理不确定空间拓扑关系推理的缺憾,将确定拓扑关系复合表推理方法扩展到模糊拓扑空间,建立了基于拓扑关系复合表的不确定拓扑关系模糊推理模型,设计了基于RCC5的拓扑关系复合表的模糊推理规则库和推理的方法流程,并结合具体计算实例对模糊推理模型进行了实验研究,推理结果验证了模型的认知合理性和可行性。     

基于拓扑关系的不确定区域方向关系推理

利用宽边界区域对多种来源的不确定性数据建模,用确定区域的拓扑和方向关系组合描述不确定区域的拓扑和方向关系。根据拓扑关系和方向关系间内在规律,直接从不确定区域的拓扑符号描述中推理出方向关系的符号描述,是对已有推理方法的有益补充。     

不确定性线状目标之间拓扑关系的描述与判别

总结了常见的不确定性线状目标描述模型以及确定性线状目标之间拓扑关系的描述模型，并在此基础上提出了一种定量化分析方法，对不确定性线状目标之间的拓扑关系进行了描述和判别。     

利用灰集进行空间区域拓扑关系不确定性定量分析

针对不确定性空间区域对象间拓扑关系不确定性的定量分析问题,提出了一种具有双隶属度函数的灰集表示模型,利用双隶属度函数的截集将不确定性空间区域对象转化为确定性区域对,借用相关的不确定性空间拓扑关系定性分析模型,将不确定性空间对象间拓扑关系的不确定性量化为一个可信度区间。     

Quantitative fuzzy topological relations of spatial objects by induced fuzzy topology

This paper presents a study on the modeling of fuzzy topological relations between uncertain objects in Geographic Information Systems (GIS). Based on the recently developed concept of computational fuzzy topological space, topological relations between simple fuzzy spatial objects are modeled. The fuzzy spatial objects here cover simple fuzzy region, simple fuzzy line segment and fuzzy point. To compute the topological relations between the simple spatial objects, intersection concepts and integration methods are applied and a computational 9-intersection model are proposed and developed. There are different types of intersection, and we have proposed different integration methods for computation in different cases. For example, surface integration method is applied to the case of the fuzzy region-to-fuzzy region relation, while the line integration method is used in the case of fuzzy line segment-to-fuzzy line segment relation. Moreover, this study has discovered that there are (a) sixteen topological relations between simple fuzzy region to line segment; (b) forty-six topological relations between simple fuzzy line segments; (c) three topological relations between simple fuzzy region to fuzzy point; and (d) three topological relations between simple fuzzy line segment to fuzzy point.     

Dealing with spatial relations for spatial objects with randomness in GIS

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.     

面目标间拓扑关系形式化描述的层次模型

拓扑关系形式化描述和区分的标准是拓扑不变量.在4交差模型的基础上,通过对两面目标边界交集的信息深入分析,提出具有不同分类能力的拓扑不变量,分别是维数、分离数、分量类型和分量排列顺序,并依次建立相应的形式化描述模型.这些模型都是在其分类层次上对面目标间拓扑关系的完备描述,并且它们的区分能力是层次递进的.     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

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.     

Topological relations between a directed line and a directed region

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.     

GIS面目标间拓扑关系的形式化模型

拓扑关系的描述和区分是建立在空间目标相互作用过程中目标集及其分量集合拓扑特性变化基础之上的.建立了GIS中拓扑关系形式化描述和区分的一种广义模型,并与已有的形式化模型进行了比较分析,结果发现现有的形式化方法和模型是广义模型的一些特例.在此基础上,进一步提出了面目标间拓扑关系形式化描述的基本模型,并根据定义的拓扑距离和拓扑复杂性概念,建立了一个新的拓扑关系概念邻域图.     

DEALING WITH SPATIAL RELATIONS FOR SPATIAL OBJECTS WITH RANDOMNESS IN GIS

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基于拓扑约束的方向关系定性描述模型

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。     

基于模糊贴近度分析的不确定拓扑关系表达模型

根据人类对模糊空间关系的认知层次性特点,将不确定拓扑关系的表达归结为求解不确定空间关系实例与基本拓扑关系概念之间的隶属度。基于贴近度分析的基本方法建立不确定地理目标之间空间拓扑关系模糊表达模型方法。结合具体的算例说明基于该模型方法的拓扑关系模糊表达推算过程,并通过与经典的Egg—Yolk以及宽边界模型方法表达的拓扑关系表达结果进行对比分析,验证模型的合理性。     

移动目标空间拓扑关系的度量描述

针对传统9交模型对移动目标空间拓扑关系表达及量化描述的不足，基于格网化思想，提出了度量参数量化描述的方法。首先对移动目标和参考目标进行格网化，划分为固定大小的格网阵列；然后利用9交模型确定空间目标间拓扑关系的形式化描述；最后引入内部分量、边界分量、外部分量3种度量参数对移动目标空间拓扑关系进行度量化描述。试验证明，该方法计算简单、直观，能更加有效地度量移动目标间的拓扑关系。     

Extended model of topological relations between spatial objects in geographic information systems

This paper presents an extended model for describing topological relations between two sets (objects) in geographic information systems (GIS). First, based on the definition of the topological relations between two objects, we uncover a sequence of topological relations between two convex sets.Second, an extended model for topological relations between two sets is proposed based on the new definition. The topological relations between two convex sets are expressed as a sequence of 4 × 4 matrices, which are the topological properties of A^o ∩ B^o, A^o\B, B^o\A, ∂A ∩ ∂B. The model is also extended for handling the properties of the topological relations between two non-convex sets, where the factor of first fundamental group is added to A ∪ B to handle these complex relations.The results show that the number of topological relations between the two sets is not as simple as finite but infinite and can be approximated by a sequence of matrices.     

多尺度表达中空间拓扑关系等价性评价模型

本文主要讨论不同尺度下地理目标之间拓扑关系的等价性问题。根据人类认知习惯,得出从大比例尺地理空间中拓扑关系抽象为小比例尺空间中拓扑关系应当遵循的基本规则,并利用模糊函数将这些规则定量化;在此基础上,得到评价地理空间场景中多个空间目标之间拓扑关系在不同尺度下是否等价的综合评价模型。该模型可以用于检验和维护空间数据质量、为地图自动综合提供空间关系处理和评价方法的参考。     

带空洞的复杂面域间拓扑关系的形式化描述

拓扑关系的描述和区分是建立在空间目标相互作用过程中目标集合及其分量集合拓扑特性变化基础之上的。对于两个简单面域间的空间拓扑关系描述研究已经很多,而对于带空洞的复杂面域间的空间拓扑关系的描述却相对较少。本文利用Egenhofer等提出的描述带空洞的面域间空间拓扑关系描述框架,在四交差模型的基础上,提出一种能描述带空洞的复杂面域间拓扑关系的扩展模型———D-4 ID模型,并用该模型推导了一个简单面域和一个带空洞的复杂面域间有意义的23种拓扑关系。