一种基于空间网格的地物要素点云提取方法

LiDAR点云的分类提取是点云数据处理中的首要步骤。为了提高复杂场景中点云数据分类提取方法的适用性,文中根据三维数学形态学思想,提出一种基于地物空间形状特征的点云提取方法。方法首先建立网格索引,划分网格空间,进行点云数据组织,然后根据地物在网格空间中的形状特征设计出四种参数可控的空间网格算子,最后结合点云反射强度信息自动提取特定地物点云。通过对复杂场景中的铁路地物要素LiDAR点云中建筑、电力杆线、铁路轨道的提取和郊区机载LiDAR点云中的地面与建筑屋顶的提取,验证提取算法的适用性,为点云分类提取功能模块的程序设计提供便捷方法。     

一种R树与格网结合的海量地铁隧道点云管理方法

为满足海量地铁隧道点云的高效处理需求，提出了一种R树与格网结合的海量地铁隧道点云管理方法。针对隧道点云的空间分布特点，在全局将大范围点云划分到格网中，并使用R树管理非空网格；在局部使用八叉树与四叉树混合的索引方法管理单个网格内的点云。为了提高点云的渲染效果，提出了基于网格面积的多细节层次结构（levels of detail，LOD）回溯构建方法，并采用高效的单文件存储方式存储点云。实验结果证明了所提出的方法在海量隧道点云的管理和可视化方面优于传统方法。     

基于三角网滤波检验的KD树粗差剔除法

针对机载Li DAR点云数据的粗差剔除和滤波,直接关系到后续数据处理的精度,本文运用KD树组织数据建立三维索引,快速查找并计算目标点与k个最近邻点的平均距离,根据距离阈值判断并剔除粗差点。实验选取3种典型测区的点云数据进行实验,分别采用形态学粗差剔除法和本文粗差剔除法对3组点云数据进行粗差剔除,并采用渐进不规则三角网滤波法对原始点云数据及两种粗差剔除结果进行滤波,对结果进行对比分析。结果验证,本文方法能有效剔除点云粗差,提高后续滤波结果的精度。     

机载激光雷达全波形数据与系统点云差异的定量分析

目前常用的小光斑机载LiDAR波形数据与系统点云数据的来源相关性较大,波形数据的优势难以严格定量地评价和比较。LeicaALS60机载LiDAR系统记录的全波形数据与点云数据相对独立,点云数据来自硬件系统脉冲探测方法,而波形数据是未加处理的原始回波序列。本文对原始波形数据进行分解获取发射脉冲的全部回波,与系统探测点云进行了定量对比,并选取典型林区和城区数据,得到波形在两种地物类型中垂直信息获取能力的定量表征参数。结果表明,波形数据对不同地物类型均能丰富垂直结构信息和提高点云垂直分辨率,且这种提高在林区表现优于城区人工建筑和裸地;激光对树木冠层的穿透能力更明显地表现在回波波形信息中,相较于传统点云激光雷达,全波形LiDAR在森林垂直参数获取方面潜力更大。     

大规模点云数据的二维与三维混合索引方法

为提高点云查询效率和按需提取数据,提出一种二维与三维混合索引的大规模点云数据管理方法。采用二维四叉树和三维最小外包盒结构管理原始点云,以3D-R树管理多站点云,利用对象关系数据库管理全部点云模型和相关属性数据。利用古建筑大规模点云数据在微机上实现了点云模型的数据存储与可视化。结果表明本方法能够管理超过10 GB级的点云模型数据和十亿级有效点,数据可视化效率较高。     

机载LiDAR点云时间纹理信息航带重叠区消冗

机载LiDAR获取的完整测区点云数据中包含了丰富的信息,同时也伴随着大量冗余数据,本文提出基于机载LiDAR点云时间纹理信息的航带重叠区消冗方法。首先按点云时间信息划分航带,再按点云纹理信息提取航带边缘,接着提取高地物遮挡空洞,最后去除重叠区冗余数据。实验结果表明,该方法无需航线信息辅助,并能在保留遮挡空洞区域点云的同时,高效地去除航带重叠区中精度较低的点云。     

Out‐of‐Core GPU‐based Change Detection in Massive 3D Point Clouds

If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out‐of‐core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point‐based rendering technique adapted for attributed 3D point clouds, to enable effective out‐of‐core real‐time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.     

基于实体模型索引的海量激光点云分块存储研究与应用

三维激光扫描技术的日益进步推动了点云数据量级的不断攀升,给海量点云的高效管理及优化处理带来新的挑战。文中针对在大范围点云场景中快速调度局部详细点云的实际应用需求,借鉴空间格网结构的数据组织思路,优化并提出可变长度的海量点云分层分条带存储的工作机制,结合数据库管理,实现了基于实体模型索引的激光点云及三维模型的快速调度及联动显示。基于研发的点云专家科研系统实践,推动了海量点云数据集约化管理水平,具有一定的科研及实用价值。     

基于探测球的固定式扫描海量点云自动定向方法

地面固定式扫描点云首先要将自由坐标系的点云纳入国家坐标系,而单站扫描的点云数据量极大,无法在可视环境下进行拼接。针对现有方法对海量点云拼接的不足,提出一种基于探测球的固定式扫描海量点云自动定向方法,该方法通过数据关联技术读取海量点云、建立标靶搜索环、球拟合确定标靶候选点、全组合距离匹配法确定同名点及坐标转换参数解算等,完成点云的自动定向过程。通过实验验证文中算法的有效性及可行性。     

基于车载点云数据的树木提取与分析

本文基于高速公路高精度点云数据，首先通过点云数据的分类处理实现对树木点云数据的提取，将树木点云投影到水平面，采用DBSCAN密度聚类算法实现单根树木的提取；然后在数据密集区域存在树木树冠点云重叠的区域，本文结合树干几何特征提取树干的位置信息，计算所有点云到树干中心的欧氏距离，将所有点云归类到最近的树干进行粗分割；最后根据粗分割的树木轮廓特征确定树冠模型与树冠中心，提出了采用基于密度特征的格网竞争算法对重叠的区域进行精细分割。试验表明，本文采用的树木分割方法能够实现单棵树木精确提取。     

全球多尺度空间数据模型研究

如何组织和管理分布式环境下全球海量（PB级以上）空间数据,进行全球多尺度空间剖分,建立高效的编码与索引机制,从而实现海量空间数据的高效调度与协同服务是网络3维虚拟地球平台中关键技术之一。对此,本文重点讨论了全球多尺度空间数据模型的建立,其核心是全球多尺度空间索引和多级金字塔模型的空间数据组织方法。最后基于开放式虚拟地球集成共享平台GeoGlobe的应用构建成＂天地图＂网站对上述方法进行了验证。     

Digital Elevation Model from the Best Results of Different Filtering of a LiDAR Point Cloud

The LiDAR point clouds captured with airborne laser scanning provide considerably more information about the terrain surface than most data sources in the past. This rich information is not simply accessed and convertible to a high quality digital elevation model (DEM) surface. The aim of the study is to generate a homogeneous and high quality DEM with the relevant resolution, as a 2.5D surface. The study is focused on extraction of terrain (bare earth) points from a point cloud, using a number of different filtering techniques accessible by selected freeware. The proposed methodology consists of: (1) assessing advantages/disadvantages of different filters across the study area, (2) regionalization of the area according to the most suitable filtering results, (3) data fusion considering differently filtered point clouds and regions, and (4) interpolation with a standard algorithm. The resulting DEM is interpolated from a point cloud fused from partial point clouds which were filtered with multiscale curvature classification (MCC), hierarchical robust interpolation (HRI), and the LAStools filtering. An important advantage of the proposed methodology is that the selected landscape and datasets properties have been more holistically studied, with applied expert knowledge and automated techniques. The resulting highly applicable DEM fulfils geometrical (numerical), geomorphological (shape), and semantic quality properties.     

车载LiDAR点云混合索引新方法

以车载LiDAR点云数据为研究对象,为提高点云数据的组织与管理效率,提出了一种全局KD树与局部八叉树相结合的混合空间索引结构—KD-OcTree。全局KD树通过分辨器、分割平面的确定,重构点云之间的邻域关系,确保索引结构的整体平衡; 在其叶子节点再构造二级索引结构—局部八叉树,避免了单一八叉树结构点云分布不均衡、树结构深度过大、出现大量无点空间等现象。以3个真实场景数据为测试数据进行试验和对比分析,结果表明,KD-OcTree混合索引不仅能够提高索引构建、邻域搜索的速度,还对分类可靠性产生一定影响。     

Comparative Analyses of the Point Cloud Produced by Using Close-Range Photogrammetry and Terrestrial Laser Scanning for Rock Surface

Point cloud produced by using theoretically and practically different techniques is one of the most preferred data types in various engineering applications and projects. The advanced methods to obtain point cloud data in terrestrial studies are close range photogrammetry (CRP) and terrestrial laser scanning (TLS). In the TLS technique, separated from the CRP in terms of system structure, denser point cloud at certain intervals can be produced. However, point clouds can be produced with the help of photographs taken at appropriate conditions depending on the hardware and software technologies. Adequate quality photographs can be obtained by consumer grade digital cameras, and photogrammetric software widely used nowadays provides the generation of point cloud support. The tendency and the desire for the TLS are higher since it constitutes a new area of research. Moreover, it is believed that TLS takes the place of CRP, reviewed as antiquated. In this study that is conducted on rock surfaces located at Istanbul Technical University Ayazaga Campus, whether point cloud produced by means photographs can be used instead of point cloud obtained by laser scanner device is investigated. Study is worked on covers approximately area of 30 m?×?10 m. In order to compare the methods, 2D and 3D analyses as well as accuracy assessment were conducted. 2D analysis is areal-based whereas 3D analysis is volume-based. Analyses results showed that point clouds in both cases are similar to each other and can be used for similar other studies. Also, because the factors affecting the accuracy of the basic data and derived product for both methods are quite variable, it was concluded that it is not appropriate to make a choice regardless of the object of interest and the working conditions.     

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.     

Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification

The features used in the separation of different objects are important for successful point cloud classification. Eigen-features from a covariance matrix of a point set with the sample mean are commonly used geometric features that can describe the local geometric characteristics of a point cloud and indicate whether the local geometry is linear, planar, or spherical. However, eigen-features calculated by the principal component analysis of a covariance matrix are sensitive to LiDAR data with inherent noise and incomplete shapes because of the non-robust statistical analysis. To obtain reliable eigen-features from LiDAR data and to improve classification accuracy, we introduce a method of analyzing local geometric characteristics of a point cloud by using a weighted covariance matrix with a geometric median. Each point is assigned a weight to represent its spatial contribution in the weighted principal component analysis and to estimate the geometric median which can be regarded as a localized center of a shape. In the experiments, qualitative and quantitative analyses on airborne LiDAR data and simulated point clouds show a clear improvement of the proposed method compared with the standard eigen-features. The classification accuracy is improved by 1.6–4.5% using a supervised classifier.     

植被稀疏地区沟蚀变化的地面激光扫描监测与沟蚀量估算

针对传统沟蚀监测手段劳作强度大,且数据采集的完整性、代表性受切沟复杂地形制约等问题,提出了一种针对植被稀疏地区沟蚀变化的地面激光扫描（terrestrial laser scanning,TLS）监测方法,形成了一套数据处理与侵蚀量计算技术流程。以河北省官厅水库东岸某大型切沟为例,利用高精度TLS进行两年3期野外监测与点云数据分析。通过点云配准、滤波、重采样及曲面拟合等预处理,生成不同采样分辨率下3期切沟表面模型,并提取地形信息;采用杨赤中滤波推估法计算并比较不同点云重采样分辨率下的沟蚀量。结果表明:（1）当点云重采样分辨率与切沟表面凹凸微结构暨石块粒径（2~6cm）接近时,沟蚀量估算值趋于稳定、结果可靠;（2）经侵蚀作用,切沟外壁表面高程整体降低2~20 cm;（3）切沟内壁侵蚀量不均衡,坡度较大处侵蚀最为显著。     

大断面点云数据提取的优化方法研究

本设计主要包括以下几个方面:一是点云数据分幅管理、建立空间索引。三维激光扫描系统获取的点云数据量大,如果直接在所有的点云数据中进行运算,那么运算效率会比较低下,且一般电脑无法处理如此大的数据。因此,必须先对原始大点云分幅处理、建立空间索引,按一定的规律形成计算机可方便处理的小文件,提高运算效率。二是整理断面数据,把断面设计信息按一定的格式整理。三是求解断面的方位、参数等信息,判断每一个横断面所经过的图幅,这一运算过程直接影响到整体效果。四是利用点云数据提取断面点。     

Spectral Filtering and Classification of Terrestrial Laser Scanner Point Clouds

A method is presented for filtering and classification of terrestrial laser scanner point clouds. The algorithm exploits the four-channel (blue, green, red and near infrared) multispectral imaging capability of some terrestrial scanners using supervised, parametric classification to assign thematic class labels to all scan cloud points. Its principal advantage is that it is a completely data-driven algorithm and is independent of spatial sampling resolution since the processing is performed in four-dimensional spectral feature space. Its application to two data-sets of different spatial extent and spatial and spectral complexity is reported, for which respective overall classification accuracies of 87·0% and 82·0% were achieved. Analysis of the input data with emphasis on the characteristics pertinent to the anticipated outcomes precedes detailed analysis of the classification results and error sources and their causes. Erroneously classified points are attributed to radiometric errors stemming from both detector hardware and physical effects.     

三维激光扫描数据获取高分辨率DTM试验研究

三维激光扫描技术能够提供实体表面点云数据,可用于获取高精度高分辨率数字地形模型。本文以重庆万州区付家岩滑坡体为例探讨了采用三维激光扫描监测技术获取数字地形模型的方法,着重讨论了相关点云数据处理流程和关键技术问题。通过结合差分GPS技术进行激光数据的相对定位和绝对定位、去噪、拼接等方法,获得了该区域地表高精度地形数据,并生成了相应的数字高程模型。试验结果初步说明该技术可用于获取小尺度高精度高分辨率数字地形模型,具有一定的应用前景。