关键点检测的复杂建筑物模型自动重建

利用机载LiDAR点云数据进行建筑物重建是当今摄影测量与遥感领域的一个热点问题,特别是复杂形状建筑物模型的精确自动构建一直是一个难题。本文提出一种基于关键点检测的复杂建筑物模型自动重建方法,采用RANSAC法与距离法相结合的分割方法自动提取建筑物屋顶各个平面的点云,并利用Alpha Shape算法提取出各个平面的精确轮廓,根据屋顶平面之间的空间拓扑关系分析建筑物的公共交线特征,在此特征约束下对提取的初始关键点进行修正,最终重建出精确的建筑物3维模型。选取不同类型复杂建筑物与包含复杂建筑物的城市区域点云进行实验,结果表明该算法具有较强实用价值。     

一种融合多源特征的建筑物三维模型重建方法

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。     

Cycle graph analysis for 3D roof structure modelling: Concepts and performance

The paper presents a cycle graph analysis approach to the automatic reconstruction of 3D roof models from airborne laser scanner data. The nature of convergences of topological relations of plane adjacencies, allowing for the reconstruction of roof corner geometries with preserved topology, can be derived from cycles in roof topology graphs. The topology between roof adjacencies is defined in terms of ridge-lines and step-edges. In the proposed method, the input point cloud is first segmented and roof topology is derived while extracting roof planes from identified non-terrain segments. Orientation and placement regularities are applied on weakly defined edges using a piecewise regularization approach prior to the reconstruction, which assists in preserving symmetries in building geometry. Roof corners are geometrically modelled using the shortest closed cycles and the outermost cycle derived from roof topology graph in which external target graphs are no longer required. Based on test results, we show that the proposed approach can handle complexities with nearly 90% of the detected roof faces reconstructed correctly. The approach allows complex height jumps and various types of building roofs to be firmly reconstructed without prior knowledge of primitive building types.     

A shape-based segmentation method for mobile laser scanning point clouds

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.     

利用层间连接和平滑策略重建屋顶三维模型

面向数字城市和智慧城市建设急需城市建筑三维模型支撑的需要,本文基于机载LiDAR数据,以“顾及平整性的屋顶面片分割—屋顶层间连接—三维模型重建”为脉络,提出了一种采用层间连接和平滑策略的建筑屋顶三维模型重建方法。在屋顶面片提取过程中,充分顾及了屋顶面片的平整性;并在屋顶面片平整基础上,提出层间连接点的概念,以实现高效、快速的模型重建工作。试验部分,本文从屋顶面片重建完整率与正确率、重建几何精度及建筑物高程对于重建的影响3个方面作了较为详尽的评价与分析,并在国际摄影测量与遥感学会标准数据集支撑下,与国际同行进行试验对比。试验结果表明,建筑屋顶重建的完整率和正确率分别达到90%和95%;在偏移距离评价方面,平均偏移距离和标准差最优分别达0.05 m和0.18 m。因此,本文方法可有效完成建筑屋顶三维模型重建,重建模型准确度高、完整性好。     

车载联合机载LiDAR点云数据的建筑物立面精细分割

从数据量庞大且散乱的车载LiDAR点云中分割出建筑物立面数据是一项繁琐而艰巨的工作。本文提出一种结合机载LiDAR点云的车载LiDAR点云建筑物立面分割方法。该方法在空-地点云严格配准的基础上,从机载LiDAR点云中分割出每栋建筑物的顶部点云,提取建筑物顶部外轮廓线并进行规则矢量化处理,设置轮廓线缓冲区实现立面点云的粗分割;再采用基于稳健特征值的平面拟合法对单栋建筑物的每个立面进行去噪滤波,实现建筑物立面的精细分割。试验结果证明了该算法对城市场景中车载LiDAR点云处理的有效性。     

机载激光扫描测高中激光脚点点群分割新方法

讨论了机载激光扫描测高激光脚点点群分割的方法，提出了一种新的分割成片房屋的激光脚点点群的方法——扫描带法，并给出了新方法的算法流程。实践表明，该算法运算速度快，分割准确、可靠。     

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.     

An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data

Laser scanning systems have been established as leading tools for the collection of high density three-dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parameterization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud – i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data.     

基于机载激光扫描数据提取建筑物的研究现状

机载激光扫描系统是集成了GPS、惯性导航系统(INS)和扫描激光测距系统并利用飞机作为运行平台,来获取地面的三维位置,进而快速生成数字表面模型(DSM)。随着机载扫描激光测距系统的不断完善和发展,获取城市DSM数据也变得越来越快速,而且方便和经济可靠,地面激光点的密度也大大提高。目前国外激光扫描系统的激光点密度一般都达到了1～20点/m2,因此利用机载激光扫描系统获取的城市DSM提取建筑物也渐渐受到重视。利用激光扫描数据提取建筑物可以分为两大类,第一类是单纯以获取的机载激光测距数据来提取建筑物,第二类是融合激光测距数据和其他相关信息的建筑物提取,如融合航空影像、融合IKONOS高分辨率卫星影像来提取建筑物。本文对国际上利用机载激光扫描测距数据进行建筑物提取的最新研究进展进行了一些分析,同时也给出了应用我国研制的机载激光扫描数据提取建筑物的试验研究和初步结果。     

A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data

This paper presents a generative statistical approach to automatic 3D building roof reconstruction from airborne laser scanning point clouds. In previous works, bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Due to the data artefacts caused by tree clutter, reflection from windows, water features, etc., the bottom-up reconstruction in urban areas may suffer from a number of incomplete or irregular roof parts. Manually given geometric constraints are usually needed to ensure plausible results. In this work we propose an automatic process with emphasis on top-down approaches. The input point cloud is firstly pre-segmented into subzones containing a limited number of buildings to reduce the computational complexity for large urban scenes. For the building extraction and reconstruction in the subzones we propose a pure top-down statistical scheme, in which the bottom-up efforts or additional data like building footprints are no more required. Based on a predefined primitive library we conduct a generative modeling to reconstruct roof models that fit the data. Primitives are assembled into an entire roof with given rules of combination and merging. Overlaps of primitives are allowed in the assembly. The selection of roof primitives, as well as the sampling of their parameters, is driven by a variant of Markov Chain Monte Carlo technique with specified jump mechanism. Experiments are performed on data-sets of different building types (from simple houses, high-rise buildings to combined building groups) and resolutions. The results show robustness despite the data artefacts mentioned above and plausibility in reconstruction.     

Automatic filtering and 2D modeling of airborne laser scanning building point cloud

This article suggests a new approach to automatic building footprint modeling using exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of the building point cloud using the bias of the Z‐coordinate histogram. This operation aims to detect the points of roof class from the building point cloud. Hence, eight rules for histogram interpretation are suggested. The second part of the suggested approach is the roof modeling algorithm. It starts by detecting the roof planes and calculating their adjacency matrix. Hence, the roof plane boundaries are classified into four categories: (1) outer boundary; (2) inner plane boundaries; (3) roof detail boundaries; and (4) boundaries related to the missing planes. Finally, the junction relationships of roof plane boundaries are analyzed for detecting the roof vertices. With regard to the resulting accuracy quantification, the average values of the correctness and the completeness indices are employed in both approaches. In the filtering algorithm, their values are respectively equal to 97.5 and 98.6%, whereas they are equal to 94.0 and 94.0% in the modeling approach. These results reflect the high efficacy of the suggested approach.     

The comparison index: A tool for assessing the accuracy of image segmentation

Segmentation algorithms applied to remote sensing data provide valuable information about the size, distribution and context of landscape objects at a range of scales. However, there is a need for well-defined and robust validation tools to assessing the reliability of segmentation results. Such tools are required to assess whether image segments are based on ‘real’ objects, such as field boundaries, or on artefacts of the image segmentation algorithm. These tools can be used to improve the reliability of any land-use/land-cover classifications or landscape analyses that is based on the image segments.The validation algorithm developed in this paper aims to: (a) localize and quantify segmentation inaccuracies; and (b) allow the assessment of segmentation results on the whole. The first aim is achieved using object metrics that enable the quantification of topological and geometric object differences. The second aim is achieved by combining these object metrics into a ‘Comparison Index’, which allows a relative comparison of different segmentation results. The approach demonstrates how the Comparison Index CI can be used to guide trial-and-error techniques, enabling the identification of a segmentation scale H that is close to optimal. Once this scale has been identified a more detailed examination of the CI–H- diagrams can be used to identify precisely what H value and associated parameter settings will yield the most accurate image segmentation results.The procedure is applied to segmented Landsat scenes in an agricultural area in Saxony-Anhalt, Germany. The segmentations were generated using the ‘Fractal Net Evolution Approach’, which is implemented in the eCognition software.     

A global optimization approach to roof segmentation from airborne lidar point clouds

This paper presents a global plane fitting approach for roof segmentation from lidar point clouds. Starting with a conventional plane fitting approach (e.g., plane fitting based on region growing), an initial segmentation is first derived from roof lidar points. Such initial segmentation is then optimized by minimizing a global energy function consisting of the distances of lidar points to initial planes (labels), spatial smoothness between data points, and the number of planes. As a global solution, the proposed approach can determine multiple roof planes simultaneously. Two lidar data sets of Indianapolis (USA) and Vaihingen (Germany) are used in the study. Experimental results show that the completeness and correctness are increased from 80.1% to 92.3%, and 93.0% to 100%, respectively; and the detection cross-lap rate and reference cross-lap rate are reduced from 11.9% to 2.2%, and 24.6% to 5.8%, respectively. As a result, the incorrect segmentation that often occurs at plane transitions is satisfactorily resolved; and the topological consistency among segmented planes is correctly retained even for complex roof structures.     

Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification

Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.     

Automatic detection of residential buildings using LIDAR data and multispectral imagery

This paper presents an automatic building detection technique using LIDAR data and multispectral imagery. Two masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect urban residential buildings, when assessed in terms of 15 indices including completeness, correctness and quality.     

Building footprint database improvement for 3D reconstruction: A split and merge approach and its evaluation

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.     

Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests

Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management.