共查询到19条相似文献,搜索用时 156 毫秒
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本文从空间-语义双重约束角度,提出一种顾及空间邻近和功能语义相似的建筑物空间分布模式识别方法。首先,基于建筑物的空间位置邻近性(即建筑物间的最小距离)约束进行聚类,获得建筑物的空间分布模式和建筑物间的空间邻近关系;然后,根据建筑物的功能语义相似性约束进行分割,获得建筑物的初步聚类结果;最后,考虑簇内相似性与簇间差异性进行整体优化,获得最终聚类结果。试验验证表明,本文方法比现有方法能够更有效地识别空间邻近与功能语义一致的建筑物群,服务于智慧城市建设中对建筑物进行语义层次综合和对城市结构进行深入研究的需求。 相似文献
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建筑群空间分布模式识别对制图综合、多尺度表达及空间数据挖掘具有重要意义.针对建筑群中以建筑物组合结构为单元的直线模式识别问题,提出一种建筑群同质二元组直线模式的识别方法.首先分析研究同质二元组直线模式的认知特征和定义;然后利用Delaunay三角网构建建筑群邻近关系,以建筑物邻近性、尺寸和方向相似性约束进行聚类,考虑邻... 相似文献
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为了使得空间聚类分析更加适应实际情况,发展了一种同时顾及空间障碍约束与空间位置邻近的空间聚类方法。该方法采用Delaunay三角网描述实体间的邻近关系,并且不依赖用户指定参数。实验验证了本方法的有效性与优越性。 相似文献
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针对制图自动综合中发现的线面数据存在的没有构建空间实体拓扑问题,本文提出一种顾及上下文的线状目标实体派生方法。首先,对围墙线、附属设施和建筑物建立空间拓扑邻近关系,对直接邻近群组进行聚类,然后,将其划分为3种模式,即两个围墙之间的拓扑连接、围墙与建筑物之间的拓扑连接及建筑物邻近的悬挂围墙线与建筑物之间的拓扑连接;最后,对这3种模式分别采用各自对应的最短路径、外轮廓线以及最近点的拓扑连接方法进行连接。以福建省数据为例进行试验,本文方法能够有效构建空间实体拓扑连接,有助于提高地图自动综合,同时能够满足生产实践的要求,具有很高的实用性。 相似文献
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特征分类与邻近图相结合的建筑物群空间分布特征提取方法 总被引:2,自引:0,他引:2
建筑物群综合过程中需要对建筑物群空间分布特征进行认知和识别。本文在分析国内外相关研究的基础上,从描述建筑物空间特征的大量指标中,利用主成份分析方法,总结并提出了有代表性的建筑物空间特征指标集:凸包面积、紧密度IPQ指标、边数和最小面积外接矩形方向,并基于这些指标研究了建筑物群的分类。在利用最小生成树邻近图(MST)划分建筑物空间子群时,考虑了建筑物成群与所处地理环境(河流和道路等因素)的关系。另外,基于最邻近图(NNG)、MST、相对邻近图(RNG)和Gabriel图(GG)4种建筑物群邻近图,提出了自动识别具有特定空间排列建筑物子群的方法,并比较分析了识别结果的影响因素和可用性。最后,选择北京某地区建筑物群为试验对象,实现了对建筑物群的分类和空间聚类,并提取了其中直线型空间排列的建筑物子群。 相似文献
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从空间数据场的角度出发,提出了一种基于场论的层次空间聚类算法(简称HSCBFT)。该算法是通过模拟空间实体间的凝聚力来描述空间实体间的相互作用,进而采取层次凝聚的策略进行聚类。通过实验分析可以发现,层次空间聚类算法具有如下优势:①空间聚类簇中各空间实体很好地满足了空间邻近且专题属性相似的要求;②能发现任意形状的空间簇,且具有良好的抗噪性;③输入参数较少。 相似文献
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居民地形状化简时,保持化简后形态与化简之前的形态相似是居民地综合的基本原则。散列式居民地是一种广泛存在、形态复杂的居民地,描述其形态并设计形态指标是关键难点问题。提出了基于制图综合原则的形态模型与形态描述指标,运用Alpha Shapes算法提取散列式居民地的边界建筑物坐标,将各个建筑物与边界的距离作为聚类指标,把建筑物划分为若干个类,形状化简时在各个类内进行选取,起到了保持居民地形态相似的目的。 相似文献
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针对高空间分辨率遥感影像震害目标难以识别和提取的问题,结合分形纹理和引力自组织神经网络(gravitational self-organizing map,g SOM),提出了一种新的面向对象分类方法。首先,利用分割算法对原始影像进行初始过分割,得到均质性较好的分割单元,以分割单元作为待处理对象;在此基础上,利用分形纹理描述待分割对象,同时融合光谱特征构建震害目标的特征向量;最后,利用g SOM对分割对象进行聚类,得到聚类结果,并利用一致性函数以最小代价将多样性的聚类结果集成,最终实现快速、自动决策分类。以四川省汶川县震后高空间分辨率遥感影像为实验数据对算法进行定性和定量的评价,结果表明,该算法能够有效地描述复杂的震害目标,既可以保持大面积震害目标的完整性,也可以反映小的震害目标及其细节信息,提高震害影像的自动分类精度。 相似文献
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多因子影响的地图居民地自动聚群与综合研究 总被引:1,自引:0,他引:1
提出了地图综合目的的居民地聚群需要遵循Gestalt的邻近性、相似性和方向性原则,描述居民地结构、形态及其关系需要6个因子,即居民地间的距离、可视区域面积、大小相似度、形状相似度、方向关系、居民地内部方向;进而运用这些原则和因子,给出了居民地的自动聚群和综合方法。 相似文献
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To visualize large urban models efficiently, this paper presents a framework for generalizing urban building footprints and facade textures by using multiple Gestalt rules and a graph-cut-based energy function. First, an urban scene is divided into different blocks by main road networks. In each block, the building footprints are partitioned into potential Gestalt groups. A footprint may satisfy several Gestalt principles. We employ the graph-cut-based optimization function to obtain a consistent segmentation of the buildings into optimal Gestalt groups with minimal energy. The building footprints in each Gestalt group are aggregated into different levels of detail (LODs). Building facade textures are also abstracted and simplified into multiple LODs using the same approach as the building footprint simplification. An effective data structure termed SceneTree is introduced to manage these aggregated building footprints and facade textures. Combined with the parallelization scheme, the rendering efficiency of large-scale urban buildings is improved. Compared with other methods, our presented method can efficiently visualize large urban models and maintain the city's image. 相似文献
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Proximity-based grouping of buildings in urban blocks: a comparison of four algorithms 总被引:4,自引:0,他引:4
Grouping of buildings based on proximity is a pre-processing step of urban pattern (structure) recognition for contextual cartographic generalization. This paper presents a comparison of grouping algorithms for polygonal buildings in urban blocks. Four clustering algorithms, Minimum Spanning Tree (MST), Density-Based Spatial Clustering Application with Noise (DBSCAN), CHAMELEON and Adaptive Spatial Clustering based on Delaunay Triangulation (ASCDT) are reviewed and analysed to detect building groups. The success of the algorithms is evaluated based on group distribution characteristics (i.e. distribution of the buildings in groups) with two methods: S_Dbw and newly proposed Cluster Assessment Circles. A proximity matrix of the nearest distances between the building polygons, and Delaunay triangulation of building vertices are created as an input for the algorithms. A topographic data-set at 1:25,000 scale is used for the experiments. Urban block polygons are created to constrain the clustering processes from topological aspect. Findings of the experiment demonstrate that DBSCAN and ASCDT are superior to CHAMELEON and MST. Among them, MST has exhibited the worst performance for finding meaningful building groups in urban blocks. 相似文献
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针对比例尺跨度较大(10倍甚至50倍)的情况,现有的聚类方法较难体现居民地的渐进合并过程。拟在初始数据源比例尺和综合后地图比例尺之间内插系列中间比例尺,在多层次上进行居民地群的识别。根据空间认知原理和格式塔视觉准则,将居民地群的空间结构概括为5种典型模式,并定义了各模式约束条件,提出了基于紧密性网络与典型模式相结合的结构化居民地群识别方法。首先,通过Delaunay三角网对大比例尺居民地要素进行邻近关系识别,建立紧密性网络图,判断强闭合环路、弱闭合环路和延伸线,识别群结构中的各类典型模式。然后对识别出的群结构进行综合处理,依据设定阈值处理得到中间各级比例尺数据,从而实现多尺度空间数据的连续可视化。实验表明,利用该方法识别出的结果能够体现居民地群的空间分布特征,更加符合人的认知习惯。 相似文献
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在建筑物群的综合过程中,建筑物群的移位是一个非常重要的任务。在分析已有算法的基础上,探讨了建筑物群移位的一个新的自适应实数编码遗传算法,在该算法中,考虑了建筑物与街道之间的空间关系和建筑物群排列在图形表达上的格式塔原则,在适应度函数的设计上也考虑了多重因素的影响,使得移位结果更符合地图制图规范和地图感受规律。实验结果表明,该算法在移位空间充足时能精确收敛到最优解,移位效果良好。 相似文献
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Recognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approaches, including three approaches that only consider proximity principle and six approaches that consider multiple grouping principles. Real-life dataset at 1:5000, 1:10,000, and 1:50,000 scales provided by National Geomatics Center of China is used to evaluate the performance of these approaches. Buildings at smaller scales are used to construct the benchmarks to test the grouping results at larger scales, and the adjusted Rand index is adopted to indicate the accuracy of the detected groups. Significant tests (Friedman test and Wilcoxon signed-rank test) are also performed to provide both the overall and pairwise comparisons of these approaches. The results show that (1) the average accuracy of most existing approaches is between 0.3 and 0.5, and the performances of these approaches are significantly different; (2) when only proximity is considered, the buffer analysis approach performs significantly better than other approaches; (3) when multiple grouping principles are considered, the local constraint-based approach usually performs better than other approaches; (4) existing approaches that consider similarity and/or continuity seldom improve the performance of building grouping. 相似文献
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On the spatial distribution of buildings for map generalization 总被引:1,自引:0,他引:1
Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. 相似文献