【作者】 程博艳；

【导师】 李小文； 刘强；

【作者基本信息】 电子科技大学 ， 检测技术与自动化装置， 2014， 博士

【摘要】 地图制图综合是地图制图学的核心问题。随着地理信息系统应用范围的不断扩展,制图综合的应用日益广泛。制图综合就是从给定的地图生成比例尺缩小的新地图,其目的是在较小的地图空间内尽可能清晰地显示目标地图的内容。制图综合过程存在着大量的形象思维和灵感思维,是地图制图学中最具有创造性的研究领域之一。传统的线性的自动化制图综合方法难以很好地全面解决这个高度智能化的难题。智能综合(Intelligence Generalization)是将能让计算机进行自动推理、学习的智能技术应用于制图综合。建筑物要素的综合是地图自动综合亟需解决的难题之一。本论文以建筑物要素的自动综合为例,分析了传统手工制图综合与自动制图综合的特点,以及合并、简化等自动综合算子的特征,着重总结了建筑物要素的自动综合原则与方法等,研究应用人工神经网络技术模拟地图专家的人脑思维,进行地图制图综合的新方法。建筑物聚类是大比例尺地图自动制图综合中建筑物合并的前提。通过分析Gestalt原理的邻近性、相似性等准则,选用建筑物重心、建筑物间的距离、建筑物与邻近线状地物要素间位置关系等参数描述建筑物,提出了基于自组织映射神经网络(Self-Organize Map,SOM)方法的地图建筑物聚类方法。在建筑合并研究中,提出了利用BP神经网络技术和栅格地图上下文语境感知实现复杂建筑物群合并的思路与方法。通过地图局部感知探测器对拟合并地图上下文语境的建筑物结构、朝向、分布、位置关系等特征进行感知,结合建筑物综合的合并原则和制图专家知识,制定出地图感知输入模式与建筑物合并多边形行进方向之间的映射规则,利用BP神经网络的学习能力实现建筑物的智能合并。试验表明,该方法能够获得令人满意的制图综合效果。与合并算法类似,本文提出了采用BP神经网络技术对栅格地图进行局部上下文语境认知的建筑物化简方法。建筑物化简就是在保持建筑物形状特征的前提下,通过减少构成建筑物多边形的节点数目消除不必要的细节,以此解决由于地图比例尺缩小而引起的建筑物自身的空间冲突问题。算法利用地图局部感知探测器认知栅格地图中建筑物的结构特征,结合建筑物化简的原则和制图专家知识,制定建筑物化简的映射规则,建立BP神经网络化简模型的训练样本库,从而指导化简模型实现建筑物的化简。与ArcGIS工具箱(ArcToolbox)中的建筑物化简工具相比较,该方法可以获得较好的化简结果。一般而言,合并与化简是制图综合过程中的两个独立操作算子,通常先进行合并再化简。尽管二者在综合过程中都有质量控制,然而如果在综合过程中,能同时进行合并与简化,即将两个操作算子耦合为一个操作算子,分析认为制图综合质量应比原有方式有所提高,然而,耦合的合并化简算子的复杂性也远远超过原有方式。因此,论文最后研究合并与化简耦合的制图综合机理,构建耦合的神经网络模型,发展了一种基于神经网络的建筑物群合并与化简耦合的新方法,以改进制图综合过程,提高制图综合质量。试验结果表明合并与化简耦合处理后的制图综合质量明显高于原有综合方式。更多还原

【Abstract】 Map generalization is one of core issues in cartography. As GIS applications expand constantly, map generalization is being applied more and more widely. Map generalization is a process of deriving a map at a reduced scale from a given map. It aims to maintain and improve the legibility of the map with less space by representing the desired map contents as distinctly as possible. As there exists a great deal of visual and inspirational thinking in map generalization, it is one of the most inventive research fields in cartography. Apparently, of those mainstream approaches to map generalization based on linear algorithms, it is difficult to achieve satisfactory qualities of automated generalization of maps with complicated areas.With the focusing on applying artificial intelligence(AI) to map generalization, the intelligente generalization technology is developed for automated building generalization, one of the most important and complicated map generalization issues. In this thesis, a novel approach for the intelligent building generalization is proposed based on neural network by simulating map expert’s thinking. Characteristics of both manual and automated generalization are analyzed, including typical automated generalization operators such as aggregation and simplification. The principles of automated building generalization are summarized as well.Building grouping is a premise of building aggregation in automated map generalization. Principles of proximity and similarity in Gestalt theory are analyzed. Then, three parameters, centroid coordinates, minimum distance between buildings, and location relationship among buildings and roads are chosen for building description. Accordingly, a method of building grouping based on self-organizing map(SOM) neural network is proposed for the building grouping.A novel back propagation neural network(BPNN) approach to building aggregation based on local perception of complicated map contexts is studied. A perceptron perceives the characteristics of building structure, orientation, distribution, and location relationships among buildings from raster maps. Also by combining rules of building aggregation and map expert knowledge, a set of mapping rules between input patterns collected by the perceptron and outputs is formulated. Once trained, the BPNN model could outline the buildings that are to be aggregated. The results are cartographically satisfactory.Similar to building aggregation, a novel approach to building simplification with raster-based local perception using the BPNN model for learning cartographer’s knowledge is studied. Cartographer’s expertise, coupled with a perceptron that perceives the characteristics of building structures in raster maps is analyzed. The relationships among local context perceived by the perceptron and outputs are formulated. After trained, the BPNN model could be applied to the simplification of buildings. Compared with the simplification tool of Arc GIS, the BPNN model could achieve better results.Aggregation and simplification are traitionally two individual operators in map generalization. However, if they are implemented simultaneously in the process of map generalization, they are coupled into one operator. The quality of generalization should exceed the traditional way. Consequently, this thesis explores a coupled mechanism of aggregation and simplification, and develops a novel aggregation and simplification combined method based on the nerual network. The results show that this coupled mechanism outperforms the traditional approach.更多还原