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Identification and Modelling of Translational and Axial Symmetries and their Hierarchical Structures in Building Footprints by Formal Grammars 
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下载免费PDF全文 Buildings and other man‐made objects, for many reasons such as economical or aesthetic, are often characterized by their symmetry. The latter predominates in the design of building footprints and building parts such as façades. Thus the identification and modeling of this valuable information facilitates the reconstruction of these buildings and their parts. This article presents a novel approach for the automatic identification and modelling of symmetries and their hierarchical structures in building footprints, providing an important prior for façade and roof reconstruction. The uncertainty of symmetries is explicitly addressed using supervised machine learning methods, in particular Support Vector Machines (SVMs). Unlike classical statistical methods, for SVMs assumptions on the a priori distribution of the data are not required. Both axial and translational symmetries are detected. The quality of the identified major and minor symmetry axes is assessed by a least squares based adjustment. Context‐free formal grammar rules are used to model the hierarchical and repetitive structure of the underlying footprints. We present an algorithm which derives grammar rules based on the previously acquired symmetry information and using lexical analysis describing regular patterns and palindrome‐like structures. This offers insights into the latent structures of building footprints and therefore describes the associated façade in a relational and compact way. 相似文献
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针对轮廓模糊建筑物多边形的化简问题,提出一种基于特征边重构的建筑物化简方法。该方法定义了建筑物的主方向和控制其整体结构的特征边,以保持建筑物的规则形态。首先利用统计加权方法计算建筑物的主方向,基于主方向对建筑物执行直角化操作。然后按照特征边的定义检测直角化建筑物的特征边,将特征边组合并抽象出几种局部结构,建立重组规则。最后通过判别特征边组合的空间关系,选择合适的结构重组规则来化简建筑物。结合真实数据进行多组试验,结果表明该方法有效还原了建筑物的直角特征,面积和形状保持良好,适用性强。 相似文献
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针对目前基于近景摄影测量方法构建建筑物立面模型过程中因密集影像匹配（DIM）点云噪声所引起的建筑物立面TIN网格模型畸变问题,本文借鉴机器学习中样本学习的思想,对建筑物立面进行了分类并对DIM点云提出了相应的滤波方法,以达到去除DIM点云噪声和改善其TIN网格模型畸变的目的。其中,针对平面结构立面,采取先对点云样本进行学习计算构建数学立面模型所需参数,再对该立面模型设定阈值并对其点云进行滤波处理的方法;针对曲面结构立面,则结合DIM点云特性先将点云样本分类标记归为立面点与非立面点,再进行样本特征值学习,使用Logistic回归算法迭代计算求解最佳回归系数,从而构建滤波分类器的方法对立面点云进行滤波处理。试验结果表明,本文滤波处理方法能将立面DIM点云噪声有效识别并去除,而且使用该方法处理后所得点云构建的建筑物立面TIN网格模型精细化程度得到有效提高,模型质量得到明显改善。 相似文献
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ABSTRACT Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used. 相似文献
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This paper presents a multi-feature based system calibration method for estimating the boresight angles of a land-based mobile mapping system (MMS) comprised of multiple two dimensional (2D) scanners. The method invokes a least-squares adjustment (LSA) to simultaneously estimate several sets of boresight angles for multiple laser scanners incorporated in an MMS as well as the parameters associated with one or more types of geometric features. This is achieved by constraining the groups of feature point clouds captured by multiple runs to fit their corresponding geometric models in such a way that the weighted sum of squares of adjustment residuals is minimized. The method is particularly suitable for in situ calibration because the geometric features involved are commonly occurring structures (e.g. building façades, bridge surfaces, highway signs and hanging power cables) that are usually captured during the actual survey. In addition to using a planar feature model for calibration, a novel and rigorous three-dimensional (3D) catenary curve model is proposed for geometric modelling of hanging cables to augment the calibration. The proposed calibrations were examined with several different combinations of groups of planar and catenary features and the resulting analysis suggests that the in situ calibrations are effective when compared to the manufacturer’s dedicated calibration, with the overall point cloud accuracies for plane fitting being 5.5 cm and 5.4 cm for the vertical and horizontal directions, respectively. It has been successfully demonstrated that the proposed method can be used in a scene having no building façades but only some long hanging cables and horizontal ground surfaces. This is particularly useful for rural areas or inter-city/provincial highways where building façades cannot commonly be captured. Parameter correlations in the calibrations were also addressed. It has also been shown that using catenary features in addition to planar features for the calibration can help de-correlate some parameters and improve the overall accuracy. The in situ nature and the high flexibility of integrating different features of the calibration make the proposed method straightforward for most end-users. 相似文献
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以激光点云数据和倾斜多视影像为研究对象，提出了一种结合机载点云、地面点云及倾斜多视纹理的融合多源特征的建筑物三维模型重建方法。该方法结合点云面元以及影像边界特征，利用倾斜影像的线特征对顶面及立面模型进行边界规则约束，实现了面元自动拓扑重建；通过交互编辑完成不同复杂程度的建筑模型重建，并对模型进行纹理映射。实验结果表明，该方法能够有效提升城市建筑物三维模型重建的效率和边界精度，为利用多源数据的空地联合建筑物三维精细重建提供了一套切实可行的解决方案。 相似文献
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Youness Dehbi Fabian Hadiji Gerhard Gröger Kristian Kersting Lutz Plümer 《Transactions in GIS》2017,21(1):134-150
The automatic interpretation of 3D point clouds for building reconstruction is a challenging task. The interpretation process requires highly structured models representing semantics. Formal grammars can describe structures as well as the parameters of buildings and their parts. We propose a novel approach for the automatic learning of weighted attributed context‐free grammar rules for 3D building reconstruction, supporting the laborious manual design of rules. We separate structure from parameter learning. Specific Support Vector Machines (SVMs) are used to generate a weighted context‐free grammar and predict structured outputs such as parse trees. The grammar is extended by parameters and constraints, which are learned based on a statistical relational learning method using Markov Logic Networks (MLNs). MLNs enforce the topological and geometric constraints. MLNs address uncertainty explicitly and provide probabilistic inference. They are able to deal with partial observations caused by occlusions. Uncertain projective geometry is used to deal with the uncertainty of the observations. Learning is based on a large building database covering different building styles and façade structures. In particular, a treebank that has been derived from the database is employed for structure learning. 相似文献
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形状识别是地图空间认知的重要内容之一,结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径。本文构建了一种融合了圆形度、偏心率和矩形度等宏观形状特征参量的傅里叶形状描述子作为形状特征向量的神经网络建筑多边形状识别器。首先,利用傅里叶变换和计算几何方法分别提取建筑多边形的傅里叶形状描述子及圆形度、偏心率、矩形度参量,并组成形状特征向量。然后,通过样本数据的训练实现了建筑多边形与形状模板之间匹配的神经网络识别器。结果表明,本文方法相较于以往的方法大幅度提高了精度(达到98.7%),而且特征提取算法不受多边形点数不一致的限制。通过对武汉、郑州两大城市的真实建筑物数据进行形状识别,证实该方法具有较好的识别效果。 相似文献
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支持地图综合的面状目标约束Delaunay三角网剖分 总被引：6，自引：0，他引：6
针对多边形面状目标的综合问题,建立了二维空间中约束Ｄｅｌａｕｎａｙ三角网剖分结构,融入多边形的环、岛屿、边界、顶点的描述,通过形式化条件检索,在该结构上提取二维空间中各种感兴趣的由剖分三角形组成的区域,用于支持地图综合中邻近多边形的搜索、多边形弯曲部位的识别、冲突关系探测、多边形合并等操作。并对基于骨架线的图结构建立、分枝宽度计算等几何问题进行了详细讨论,指出了其在诸如双线河中轴化、街道中轴线网络模型建立、多边形合并中的邻近关系分析、面状目标注记自动定位领域的支持作用。 相似文献
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深度学习提取高分辨率遥感影像中的建筑物信息容易受到物体周围的阴影、植被等噪声干扰而使结果存在边界锯齿化、建筑物整体不规整等问题。本文提出了利用符合建筑物边界轮廓的最小外接矩形最大限度地拟合建筑物轮廓的思路。首先利用深度学习和建筑物验证处理得到的建筑物信息,对建筑物边界利用垂距法进行多边形的拟合;然后对多边形的最小外接矩形进行筛选,选取最合适的最小外接矩形边线段作为新的边界轮廓,以提高提取的精度。对多幅遥感影像进行了实验,结果表明,本文所提出的方法提高了深度学习提取的建筑物边界轮廓准确性,能更逼近真实建筑物的边界轮廓。 相似文献
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利用分类方法快速更新基础地理信息数据是保持其现势性的一种重要方法,分类后变化检测是其中一个难点.文章提出一种基于几何特征的面状地理要素变化检测方法,将提取的面状地理要素与待更新要素对比,计算两者多边形质心距离、形状系数、周长和面积相似程度等几何特征来判断要素是否发生变化,从而进行更新.实验结果表明,该方法能处理分类后多... 相似文献
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Bruno Vallet Marc Pierrot-DeseillignyDidier Boldo Mathieu Brédif 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):732-742
We present a general framework to improve a vectorial building footprint database consisting of a set of 2D polygons. The aim of this improvement is to make the database more proper to subsequent 3D building reconstruction at a large scale. Each polygon is split into several simple polygons guided by a digital elevation model (DEM). We say that this segmentation is vectorial as we produce segmentations that intrinsically have simple polygonal shapes, instead of doing a raster segmentation of the DEM within the polygon then trying to simplify it in a vectorization step. The method is based on a Mumford and Shah like energy functional characterizing the quality of the segmentation. We simplify the problem by imposing that the segmentation edges have directions present in the input polygon over which the DEM is defined. We evaluate the validity of the proposed method on a very large dataset and discuss its pros and cons based on this evaluation. 相似文献
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《International Journal of Digital Earth》2013,6(8):785-797
ABSTRACTRepetitive structures of a building share features in terms of geometries and appearance and, therefore, the 3D information for these structures can be transferred from one specification to another for the purpose of 3D modeling and reconstruction once they are identified as repetitive structures. In this paper, a novel approach is proposed for the detection of the repetitive structures specified by the polygons of a building’s footprints. Instead of directly operating on the polygon in 2D space, the polygon is converted into a bend angle function representation in 1D space, whereby an extrusion is represented as a closed polygon intersected by the x-axis and located above it, while an intrusion is represented as a closed polygon below the x-axis. In this way, a polygon of a footprint is decomposed into a number of extrusions and intrusions which can in turn be processed. The task of detecting any repetitive structures specified in a building’s footprints then becomes the task of clustering the intersected polygons in the bend angle function space. The extrusions/intrusions which can be placed in the same clusters can be regarded as repetitive structures. Experiments show that this proposed approach can detect repetitive structures with different sizes, orientations and complexities. 相似文献
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在地图综合中,许多建筑多边形化简的方法都是针对于直角多边形的,建筑多边形的直角化也是地图数据在进入GIS之前对数据完整性检验的必经过程.本文介绍了一种利用条件极值来计算直角地物中各点坐标改正数的方法.该方法成功用于大比例综合缩编软件GenTool,并获得了满意的效果. 相似文献
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根据建立大比例尺多级土地利用数据库的需要,在进行面状道路数据综合时,对道路进行分类,保留需保持面状的道路面,将不符合要求的道路从网络状道路面中分离出来,并根据需要予以转线或删除,形成新的小比例尺的线面混合道路数据。同时,将删除的道路面分割并归并进相邻图斑,以保证土地数据面状连续的特点。 相似文献
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面向空间数据连续地图综合问题,提出了一种基于骨架线端点匹配的面状要素渐变方法,通过在两个关键表达之间进行尺度内插,实时、动态地派生任意中间比例尺地图数据。首先,对面状要素在大小比例尺下的两重表达分别进行约束Delaunay三角网剖分并提取各自的骨架线特征;然后,使用最优子序双射优化技术对骨架端点进行匹配获得多边形边界上相对应的特征点序列;最后,在剖分边界的基础上进行分段常规线性内插,获得面状要素介于始末尺度之间的多尺度表达。实验结果表明,该算法充分顾及了空间数据弯曲结构特征,对于光滑边界面状要素的渐变变换具有良好的渐变效果,可用于空间数据的连续地图综合和多尺度表达。 相似文献
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建筑物白模多边形数据可广泛应用于许多领域,但在实际应用中,由于数据太过细致,且目前使用的建筑物白模多边形数据存在拓扑关系错误,不满足生产要求,这给地图综合中建筑物群的自动合并提出了新的要求。因此提出了一种基于约束性Delaunay三角网的建筑物白模多边形自动合并方法,在保持建筑物整体结构和视觉效果的前提下减少不必要的细节。该方法能够消除原始建筑物图形数据中的空间拓扑关系错误,并对建筑物白模多边形进行一致性处理,有利于独栋整体建筑物的三维可视化。并用C#编程语言开发桌面应用程序对该方法进行了实现,通过和ArcGIS建筑物综合工具的建筑物群合并结果进行对比分析,验证了该方法的有效性和实用性。 相似文献