空间数据几何相似性度量理论方法与应用研究
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摘要
相似性是人类感知、判别、分类和推理等认知活动的基础。空间数据相似性度量不仅是地理信息数据融合更新、空间数据检索和相似查询、空间聚类和异常探测、电子地图导航等技术的核心和基础,而且也是从更深层次探寻地理实体在不同历史时期的相似形成、演变与差异性规律,进而进行动态相似推理、相似预测和知识发现的关键。本文主要研究多源多尺度、多时相空间数据几何相似性度量理论方法及其应用,所做的主要工作及取得的成果有:
1、总结了相似性科学与相似工程的研究进展,分析了几何相似性在计算机视觉和地理信息科学领域的研究现状和面临的挑战。提出了论文研究的基本思路,即:空间数据几何相似性度量模型的建立以相似性科学与工程为基础,借鉴图形图像处理方面的算法和空间关系理论,考虑到空间数据的特殊性,对上述理论方法进行大量的改进和创新。
2、论文基于心理学、认知科学、思维科学和系统科学,论述了相似性的定义、性质和分类,介绍了基于距离的相似性度量、基于系统的相似性度量和几种典型的几何相似性度量模型。然后运用上述基本理论讨论了空间数据相似性的科学内涵、概念和基本性质,并从地理实体变化的角度分析了空间数据相似性产生的根源,即:变化导致空间数据产生差异性和相似性。从系统的角度分析和定义了空间数据相似性,即把空间数据看作一个整体的系统,然后划分了系统的层次结构,阐述了系统-要素-特征之间的逻辑关系。在系统分析的基础上,对空间数据相似性进行了层次分类和数学上的形式化定义,得出“空间数据相似性度量是基于系统的相似性度量方法与基于距离的相似性度量方法的有机结合”这一重要结论。
3、建立了矢量目标间几何相似性度量的多种模型,研究了几何相似性度量模型在空间目标匹配、合并与制图综合中具体应用,主要包括:提出了一种基于KL特征的空间点集目标间的相似匹配方法,建立了点集目标间的几何相似性度量模型,最后在点集匹配的基础上利用最优插值方法合并了点目标,提高了数据精度;利用平均Fréchet距离来度量曲线的几何相似性(该方法还可以识别同名曲线上的邻近点),基于此相似性度量方法,提出了一种不同比例尺地图数据网状线要素匹配算法,即先进行结点、弧段的粗匹配,然后利用结点—弧段拓扑关系的相似性和离散Fréchet距离进行精确匹配,匹配过程将几何、语义、拓扑、结点和弧段匹配有效结合起来,在匹配的基础上,合并了同名线目标;利用多级弦长函数和中心距离函数从全局整体到局部细节逐级描述面目标几何形状,建立了通用多尺度面目标几何相似性度量模型。并基于高斯概率统计模型改进了传统的Hausdorff距离,引入信息检索中的相关反馈技术解决了相似度量模型中各指标阈值的确定问题,最后将相似度量模型分别应用于不同比例尺数据匹配和空间目标化简前后的相似度量。
4、基于矢量数据轮廓与其对应影像数据边缘轮廓的几何相似性,研究了如何利用主动轮廓模型和已有的矢量数据来提取更新面目标和线目标矢量数据。首先对传统主动轮廓模型的基本原理、求解方法、优点及目前存在的问题做了简要介绍。然后提出一种用于提取面状水体和线状道路的新的主动轮廓模型,新模型在传统内部能量和外部能量的基础上加入基于目标-背景灰度的图像引力势能和基于离散曲率的相似约束势能,目的分别是为了提高模型的收敛速度和抗噪性能,避免噪声点对轮廓曲线上点的吸引和干扰进而使曲线变形太大。并充分利用矢量数据的先验信息,自适应确定模型中的相关参数,还在原始直线主动轮廓模型的基础上加入“气球”膨胀力以提取更新线目标。同时,建立了基于相似性度量的提取精度评估模型,给出了基于贪婪算法的模型求解过程。
5、研究了空间数据集合间的拓扑关系、方向关系和五元混合相似性度量模型的建立。首先基于9交集模型总结计算了各类拓扑关系之间的距离值,在拓扑关系距离的基础上建立了实体集合间简单拓扑关系的相似性度量模型。基于简单实体集合间拓扑关系的相似性度量,采用“分解—组合”的思路建立了实体集合间复杂拓扑关系的相似性度量模型;基于方向关系矩阵的方向关系描述方法,改进了Goyal的方向关系概念邻域图,使之更符合人对方向关系的认知理解,并在此基础上建立了一般情况下实体对之间方向关系的相似性度量模型,如1:N、N:N和M:N空间方向关系之间的相似性,然后建立了实体集合间空间方向关系的相似性度量模型。基于数量、维数、几何、拓扑关系和方向关系相似性度量模型建立了五元组混合相似性度量模型。将混合相似性度量模型应用于地理要素的变化推理中,建立了存储和描述地理要素变化的地理事件模型,并基于混合相似性度量模型给出了地理事件的推理过程。最后将本文的研究成果集成于数字地图生产与更新的实验系统中,介绍了实验系统的基本情况和主要功能。
The similarity is the basis of the human perception, identification, classification, reasoning and other cognitive activities. The similarity measure of spatial data is not only the key technologies of map navigation, spatial data matching, spatial data fusion, spatial data updating, spatial data retrieval and similarity query, spatial data clustering and anomaly detection, but also it has very important significance in exploring the variation laws of geographic entities in different period and implementing dynamic prediction. This dissertation focuses on how to establish and apply geometric similarity measurement model for multi–source, multi–scale and multi–temporal spatial data. The main contributions are as follows:
1. The dissertation summarizes the research actuality of similarity science and engineering, and geometric similarity applications, actuality, challenges and solution in the field of computer vision and geography information science are also present. This dissertation thinks that the theory basis of spatial data geometric similarity measurement model establishment is what similarity science and engineering includes interrelated theory, and the key algorithms are based on image and spatial relations processing algorithms, but the particularity of spatial data must be take into account.
2. The dissertation discusses the basic definition, property and classification about similarity based on psychology, cognition science, noetic science and system science, and then the similarity measurement distance–based, similarity measurement system–based and several typical geometric similarity measurement models are also introduced. Based on the above basic theory, this dissertation presents the scientific meaning, concept, basic property for spatial data similarity, and the causes of spatial data similarity are analyzed from the geography entity changes. Then the dissertation analyses and defines spatial data similarity from the system point of view, i.e. the spatial data was seen as a system, which is divided into many hierarchies. The logic relations of system–element-feature about are spatial data similarity explained. Based on system analysis, this dissertation conducts a hierarchical classification and formal definition mathematically, and the important conclusion that the similarity measurement methods for spatial data are the organic integration of similarity measurement distance–based and similarity measurement system–based is got.
3. The dissertation establishes kinds of geometric similarity measurement models between the vector objects, and researches on the models applications in spatial objects matching, conflation and cartographic generalization, includes: this dissertation introduces the basic idea and method of point pattern matching and proposes similarity matching method of between spatial point group objects based on KL feature, geometric similarity measurement model of point group objects is established. Finally, the dissertation applies the optimum interpolation method to conflate the multi-source geography spatial data based on point group matching, compared with other methods, this method can improve the data precision; the dissertation presents a method of curve similarity measurement based on average Fréchet distance(the method can also identify neighborhood points between homonymy curves). This dissertation proposes an algorithm for feature matching from network data at different map scaled based on similarity measure. The whole strategy of matching is the first pre-matching of nodes and arcs, followed by accurate matching through similarity of node-arc topologies and discrete Fréchet distance. The matching process combines the matches in geometry, semantics, topology, nodes and arcs effectively. Based on matching, line objects are conflated; the dissertation establishes universal measure model of geometry similarity for multi-scale spatial data based on multilevel chord length functions and center distance functions. These functions can describe geometry shape from entirety to part gradually. This dissertation improves the traditional Hausdorff distance based on the statistic Gaussian mode. The enactment of every criteria threshold value in the measure model of geometry similarity is solved by introducing relevance feedback techniques. At last, the model is applied in data matching of different scales and similarity measure of spatial object simplification.
4. The dissertation researches on how to apply active contour model and existing vector data extracting and updating area and linear objects based on the geometry similarity between the vector data contour and image data edge contour. The basic theory, solving methods, advantages and disadvantages of the traditional active contour model are introduced. This dissertation presents a new active contour model, which is used to extracting area water body and linear road from Remote Sensing Images. The new active contour model is added to the image gravitation potential energy based on object-background gray value and the similarity restriction potential energy based on discrete curvature, the purpose is to improve the model constringency speed and noise immunity, and to avoid the noise on the curve point of attraction and disturbance. The model use prior information of vector data adequately to compute correlative parameter adaptively. The balloon power is added into original line active contour model to extract and update linear objects. At the same time, The extraction precision evaluating model is established based on similarity measurement, and the process of solving the model is given based on greedy algorithm.
5. The dissertation researches on the establishment of topological relations similarity measurement model, direction relations similarity measurement model and five–tuples similarity measurement model. Based on 9–intersection classification and description of topological relations, the distance values between various topological relations are computed and the simple topological relations similarity measurement model between objects set is established. This dissertation establish similarity measurement model of complex topological relations by using the strategy of decomposing– combination based on simple topological relations similarity measurement model; Goyal’s conceptual neighborhood graph is improved and the normal direction relations similarity measurement model is established based on direction relations describing methods, such as 1:N, N:N and M:N direction relations similarity. Then direction relations similarity measurement model between objects set is established, and finally, this dissertation establishes five–tuples similarity measurement model based on quantity, dimension, geometry, topological relations and direction relations similarity measurement model. The reasoning process of geography event is presented based on mixed similarity measurement model and the database logical expression frame and the detection and storage flow for geographical event is designed. At last, the dissertation’s research are used in map making and geography information updating system, and the basic content and main function of experiment system are also introduced.
1、总结了相似性科学与相似工程的研究进展,分析了几何相似性在计算机视觉和地理信息科学领域的研究现状和面临的挑战。提出了论文研究的基本思路,即:空间数据几何相似性度量模型的建立以相似性科学与工程为基础,借鉴图形图像处理方面的算法和空间关系理论,考虑到空间数据的特殊性,对上述理论方法进行大量的改进和创新。
2、论文基于心理学、认知科学、思维科学和系统科学,论述了相似性的定义、性质和分类,介绍了基于距离的相似性度量、基于系统的相似性度量和几种典型的几何相似性度量模型。然后运用上述基本理论讨论了空间数据相似性的科学内涵、概念和基本性质,并从地理实体变化的角度分析了空间数据相似性产生的根源,即:变化导致空间数据产生差异性和相似性。从系统的角度分析和定义了空间数据相似性,即把空间数据看作一个整体的系统,然后划分了系统的层次结构,阐述了系统-要素-特征之间的逻辑关系。在系统分析的基础上,对空间数据相似性进行了层次分类和数学上的形式化定义,得出“空间数据相似性度量是基于系统的相似性度量方法与基于距离的相似性度量方法的有机结合”这一重要结论。
3、建立了矢量目标间几何相似性度量的多种模型,研究了几何相似性度量模型在空间目标匹配、合并与制图综合中具体应用,主要包括:提出了一种基于KL特征的空间点集目标间的相似匹配方法,建立了点集目标间的几何相似性度量模型,最后在点集匹配的基础上利用最优插值方法合并了点目标,提高了数据精度;利用平均Fréchet距离来度量曲线的几何相似性(该方法还可以识别同名曲线上的邻近点),基于此相似性度量方法,提出了一种不同比例尺地图数据网状线要素匹配算法,即先进行结点、弧段的粗匹配,然后利用结点—弧段拓扑关系的相似性和离散Fréchet距离进行精确匹配,匹配过程将几何、语义、拓扑、结点和弧段匹配有效结合起来,在匹配的基础上,合并了同名线目标;利用多级弦长函数和中心距离函数从全局整体到局部细节逐级描述面目标几何形状,建立了通用多尺度面目标几何相似性度量模型。并基于高斯概率统计模型改进了传统的Hausdorff距离,引入信息检索中的相关反馈技术解决了相似度量模型中各指标阈值的确定问题,最后将相似度量模型分别应用于不同比例尺数据匹配和空间目标化简前后的相似度量。
4、基于矢量数据轮廓与其对应影像数据边缘轮廓的几何相似性,研究了如何利用主动轮廓模型和已有的矢量数据来提取更新面目标和线目标矢量数据。首先对传统主动轮廓模型的基本原理、求解方法、优点及目前存在的问题做了简要介绍。然后提出一种用于提取面状水体和线状道路的新的主动轮廓模型,新模型在传统内部能量和外部能量的基础上加入基于目标-背景灰度的图像引力势能和基于离散曲率的相似约束势能,目的分别是为了提高模型的收敛速度和抗噪性能,避免噪声点对轮廓曲线上点的吸引和干扰进而使曲线变形太大。并充分利用矢量数据的先验信息,自适应确定模型中的相关参数,还在原始直线主动轮廓模型的基础上加入“气球”膨胀力以提取更新线目标。同时,建立了基于相似性度量的提取精度评估模型,给出了基于贪婪算法的模型求解过程。
5、研究了空间数据集合间的拓扑关系、方向关系和五元混合相似性度量模型的建立。首先基于9交集模型总结计算了各类拓扑关系之间的距离值,在拓扑关系距离的基础上建立了实体集合间简单拓扑关系的相似性度量模型。基于简单实体集合间拓扑关系的相似性度量,采用“分解—组合”的思路建立了实体集合间复杂拓扑关系的相似性度量模型;基于方向关系矩阵的方向关系描述方法,改进了Goyal的方向关系概念邻域图,使之更符合人对方向关系的认知理解,并在此基础上建立了一般情况下实体对之间方向关系的相似性度量模型,如1:N、N:N和M:N空间方向关系之间的相似性,然后建立了实体集合间空间方向关系的相似性度量模型。基于数量、维数、几何、拓扑关系和方向关系相似性度量模型建立了五元组混合相似性度量模型。将混合相似性度量模型应用于地理要素的变化推理中,建立了存储和描述地理要素变化的地理事件模型,并基于混合相似性度量模型给出了地理事件的推理过程。最后将本文的研究成果集成于数字地图生产与更新的实验系统中,介绍了实验系统的基本情况和主要功能。
The similarity is the basis of the human perception, identification, classification, reasoning and other cognitive activities. The similarity measure of spatial data is not only the key technologies of map navigation, spatial data matching, spatial data fusion, spatial data updating, spatial data retrieval and similarity query, spatial data clustering and anomaly detection, but also it has very important significance in exploring the variation laws of geographic entities in different period and implementing dynamic prediction. This dissertation focuses on how to establish and apply geometric similarity measurement model for multi–source, multi–scale and multi–temporal spatial data. The main contributions are as follows:
1. The dissertation summarizes the research actuality of similarity science and engineering, and geometric similarity applications, actuality, challenges and solution in the field of computer vision and geography information science are also present. This dissertation thinks that the theory basis of spatial data geometric similarity measurement model establishment is what similarity science and engineering includes interrelated theory, and the key algorithms are based on image and spatial relations processing algorithms, but the particularity of spatial data must be take into account.
2. The dissertation discusses the basic definition, property and classification about similarity based on psychology, cognition science, noetic science and system science, and then the similarity measurement distance–based, similarity measurement system–based and several typical geometric similarity measurement models are also introduced. Based on the above basic theory, this dissertation presents the scientific meaning, concept, basic property for spatial data similarity, and the causes of spatial data similarity are analyzed from the geography entity changes. Then the dissertation analyses and defines spatial data similarity from the system point of view, i.e. the spatial data was seen as a system, which is divided into many hierarchies. The logic relations of system–element-feature about are spatial data similarity explained. Based on system analysis, this dissertation conducts a hierarchical classification and formal definition mathematically, and the important conclusion that the similarity measurement methods for spatial data are the organic integration of similarity measurement distance–based and similarity measurement system–based is got.
3. The dissertation establishes kinds of geometric similarity measurement models between the vector objects, and researches on the models applications in spatial objects matching, conflation and cartographic generalization, includes: this dissertation introduces the basic idea and method of point pattern matching and proposes similarity matching method of between spatial point group objects based on KL feature, geometric similarity measurement model of point group objects is established. Finally, the dissertation applies the optimum interpolation method to conflate the multi-source geography spatial data based on point group matching, compared with other methods, this method can improve the data precision; the dissertation presents a method of curve similarity measurement based on average Fréchet distance(the method can also identify neighborhood points between homonymy curves). This dissertation proposes an algorithm for feature matching from network data at different map scaled based on similarity measure. The whole strategy of matching is the first pre-matching of nodes and arcs, followed by accurate matching through similarity of node-arc topologies and discrete Fréchet distance. The matching process combines the matches in geometry, semantics, topology, nodes and arcs effectively. Based on matching, line objects are conflated; the dissertation establishes universal measure model of geometry similarity for multi-scale spatial data based on multilevel chord length functions and center distance functions. These functions can describe geometry shape from entirety to part gradually. This dissertation improves the traditional Hausdorff distance based on the statistic Gaussian mode. The enactment of every criteria threshold value in the measure model of geometry similarity is solved by introducing relevance feedback techniques. At last, the model is applied in data matching of different scales and similarity measure of spatial object simplification.
4. The dissertation researches on how to apply active contour model and existing vector data extracting and updating area and linear objects based on the geometry similarity between the vector data contour and image data edge contour. The basic theory, solving methods, advantages and disadvantages of the traditional active contour model are introduced. This dissertation presents a new active contour model, which is used to extracting area water body and linear road from Remote Sensing Images. The new active contour model is added to the image gravitation potential energy based on object-background gray value and the similarity restriction potential energy based on discrete curvature, the purpose is to improve the model constringency speed and noise immunity, and to avoid the noise on the curve point of attraction and disturbance. The model use prior information of vector data adequately to compute correlative parameter adaptively. The balloon power is added into original line active contour model to extract and update linear objects. At the same time, The extraction precision evaluating model is established based on similarity measurement, and the process of solving the model is given based on greedy algorithm.
5. The dissertation researches on the establishment of topological relations similarity measurement model, direction relations similarity measurement model and five–tuples similarity measurement model. Based on 9–intersection classification and description of topological relations, the distance values between various topological relations are computed and the simple topological relations similarity measurement model between objects set is established. This dissertation establish similarity measurement model of complex topological relations by using the strategy of decomposing– combination based on simple topological relations similarity measurement model; Goyal’s conceptual neighborhood graph is improved and the normal direction relations similarity measurement model is established based on direction relations describing methods, such as 1:N, N:N and M:N direction relations similarity. Then direction relations similarity measurement model between objects set is established, and finally, this dissertation establishes five–tuples similarity measurement model based on quantity, dimension, geometry, topological relations and direction relations similarity measurement model. The reasoning process of geography event is presented based on mixed similarity measurement model and the database logical expression frame and the detection and storage flow for geographical event is designed. At last, the dissertation’s research are used in map making and geography information updating system, and the basic content and main function of experiment system are also introduced.
引文
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