基于知识规则构建和形态学修复的建筑物提取研究

提出了一种基于知识规则构建和形态学修复的建筑物提取方法,并以青岛市崂山区作为试验区开展建筑物信息提取研究.首先,选用试验区的快鸟影像,进行影像预处理、小尺度分割、大尺度合并、知识规则构建等算法处理,得到建筑物轮廓的粗提取结果,对提取结果经过形态学修复和边缘检测后,得到粗提取的建筑物轮廓矢量图;然后在以ArcGIS Engine为平台开发的建筑物提取系统中,以预处理后影像和第一步中获取的建筑物矢量图作为双底图,针对建筑物的不同形态,分别采用手扶跟踪数字化、自动跟踪数字化和模型数字化来规则化处理建筑物轮廓;最后获得建筑物轮廓的精提取结果.试验结果表明,与监督分类方法相比,这种方法提取出的建筑物轮廓清晰完整、精度高、速度快,提高了建筑物提取的自动化和智能化水平.     

面向塔式建筑物的无人机环拍三维建模方法研究

塔式建筑物具有结构高大细长、形状不规则及纹理信息丰富等特征,对其三维建模的难度较大。该文提出一种无人机环拍摄影的三维建模方法,即选用小型旋翼无人机搭载单相机,以环拍摄影的方式获取影像并构建实景三维模型。以云南大学塔式建筑为研究对象,采用环拍摄影和倾斜摄影两种方式获取影像,分别构建塔式建筑物的三维模型,并从控制点精度、纹理细节信息及模型复杂度三方面对比两类影像的建模效果。结果表明:基于环拍摄影的塔式建筑物三维建模效果优于基于倾斜摄影的三维建模效果,其模型纹理细节缺失少、控制点精度较好、完整度高。     

一种基于形状文法的建筑物三维建模新方法

现有三维建模方法存在侧重可视化表现、忽略模型与建筑物结构规模之间有机联系、细节层次缺乏伸缩性等问题.该文根据建筑构造知识,提出基于形状文法的建筑物三维建模方法,开展建筑物主体建模、纹理细节构建及附属设施建模.实验证明,该方法能提高建筑物三维模型的可重用性和灵活性,在室内外建筑物模型动态生成、城市规划设计等方面有实用前景.     

摄影测量三维重建中多源信息融合方法探讨

分析摄影测量三维重建中不同数据源的特点,针对现有多源数据融合存在的有限数据源低层次融合问题,提出一种基于影像特征、GIS信息和3D TIN模型融合的层次结构与算法流程 .该算法主要包括多边形区域增长、几何推理和建筑物几何模型平差等处理,为基于摄影测 量的三维城市模型自动重建提供了一种新的思路与方法.     

三维重磁人机交互解释的剖面成体建模方法

由于重磁异常人机交互解释过程中三维地质体模型的构建和编辑操作在二维计算机屏幕上很困难,给物探解释人员带来很大的不便.该文提出了一种将各个剖面上多边形连接成体的建模方法,给出了该地质体模型的正演计算方法,并用VC6.0与OpenGL在开发的三维重磁正反演解释系统中进行了实现.借助该方法,重磁解释人员可方便地编辑地质体模型并且可直观地进行三维显示,从而提高人机交互解释的效率.     

面向对象的地理遥感信息模型

地理遥感信息模型是在地形模型、物理模型、数学模型的基础上提出的一种新型模型。面向对象技术是一种新型的系统建模技术。该文论述面向对象技术应用于地理遥感信息模型建模的可行性 ,以面向对象技术基本理论为指导 ,采用面向对象分析方法 ,将地理事物分类、抽象成具有一定地理意义的独立因子团 (即面向对象模型中的对象和类 )。提出了面向对象的地理遥感信息模型的建模方法、数据结构以及建模优势     

基于超像素的高分辨率影像建筑物提取

建筑物提取对于基础地理数据获取与更新具有重要意义。针对传统的基于像元方法难以利用影像空间、上下文信息以及面向对象方法最优分割尺度难以确定等问题,该文提出一种基于超像素的高分辨率影像建筑物提取方法。首先利用LiDAR点云数据生成归一化高程模型以获取地物的高度特征,并通过Layerstacking方式与高分辨率影像融合;随后基于Simple Linear Iterative Clustering(SLIC)算法分割高分辨率影像的RGB彩色图像生成超像素;继而基于分割所得的超像素中的每个像元对超像素进行特征计算;最后使用基于RBF核的支持向量机方法进行超像素级别分类,得到建筑物提取结果。实验结果表明,该方法能够有效识别出简单场景和复杂场景下的建筑物,显著消除了传统基于像元方法出现的"椒盐效应",同时避免了面向对象方法中最佳分割尺度选择的难题,取得了较好的建筑物提取效果。     

面向对象的地理遥感信息模型

地理遥感信息模型是在地形模型，物理模型。数学模型上提出的一种新型模型。面向对象技术是一种新型的系统建模技术，该文论述面向对象技术应用于地理遥感信息模型建模的可行性，以面向对象技术基本理论为指导，采用面向对象分析方法，将地理事物分类，抽象成具有一定地理意义的独立因子团（即面向对象模型中的对象和类）。提出了面向对象的地理遥感信息模型的建模方法，数据结构以及建模优势。     

一种基于拓扑三角形集的地上下空间数据集成方法

地上下空间数据集成是数字城市、数字矿山与数字岩土工程建设中地上下无缝集成建模的关键.在空间数据集成内涵分析的基础上,讨论了地上下空间数据集成的机制与原理,提出地上下空间数据集成的初步方案.通过对Open GIS简单要素规范进行扩展,强调"体"的引入对三维空间数据集成的重要性,针对地上建筑、地形和地层建模分别采用CSG、CD-TIN和GTP模型的特例.提出了对CSG+CD TIN+GTP模型数据进行集成的数据组织结构;并且设计了一种基于拓扑三角形集(TTS)进行地上下空间数据集成的技术框架,提出集成模型数据表达的方法.结合某城市地上下空间建模应用实例,给出基于TTs进行地上下空间数据集成与建模的效果.     

基于动态椭圆的LiDAR数据窗户边界提取研究

针对复杂建筑物立面中窗户精细提取的难题,该文基于车载LiDAR数据提出一套完整的不规则窗户边界提取方法:先通过RANSAC算法探测建筑物主墙面点云,借助语义特征分离墙面和非墙面点云;再通过坐标变换,将非墙面点云转换到水平面,采用格网对水平面内点集的邻域关系进行判断,使用平面八邻域连通性探测方法对二维平面内窗户点进行聚类,分别存储聚类后的每一个窗户点云;然后采用改进的动态椭圆凸壳算法,检测聚类后各窗户边界轮廓点;最后对窗户点云坐标逆变换,得到建筑物立面中的窗户边界。通过实验验证了该算法的准确性。     

3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database

Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance.     

基于正交多项式的平原城区机载LiDAR数据滤波算法

从机载LiDAR点云数据中分离地面点与非地面点生成城区DEM,是构建数字城市的首要工作。该文对机载LiDAR数据处理中的滤波关键算法进行了研究。首先,利用常用的区域增长方法对机载LiDAR点云数据进行了滤波处理,然后基于正交多项式分带滤波方法进行了点云滤波,比较了两种算法的运行效率,分析了二者的特点,并通过试验对比进行了阈值参数优选,试验表明该算法具有较好的滤波效果,在平原城区的数字城市三维建模中具有很好的应用价值。     

High‐resolution mapping of Manawatu palaeochannels

New remote sensing techniques, such as airborne laser scanning (LiDAR), have led to a dramatic increase in terrain information, providing new opportunities for landform analysis. A major advance in using LiDAR‐derived high‐resolution topography (HRT) is the capability to provide an accurate and detailed terrain morphology. This study aims to use LiDAR HRT to identify palaeochannels of the Manawatu River (New Zealand) using an automated procedure based on the statistical analysis of landform curvature. The approach can provide rapid assessment and classification of floodplain topography. The proposed analysis is crucial, especially for intensively used floodplains requiring effective flood management and mitigation.     

Spatial structure analysis of a reptile community with airborne LiDAR data

The analysis of the spatial structure of animal communities requires spatial data to determine the distribution of individuals and their limiting factors. New technologies like very precise GPS as well as satellite imagery and aerial photographs of very high spatial resolution are now available. Data from airborne LiDAR (Light Detection and Ranging) sensors can provide digital models of ground and vegetation surfaces with pixel sizes of less than 1 m. We present the first study in terrestrial herpetology using LiDAR data. We aim to identify the spatial patterns of a community of four species of lizards (Lacerta schreiberi, Timon lepidus, Podarcis bocagei, and P. hispanica), and to determine how the habitat is influencing the distribution of the species spatially. The study area is located in Northern Portugal. The position of each lizard was recorded during 16 surveys of 1 h with a very precise GPS (error < 1 m). LiDAR data provided digital models of surface, terrain, and normalised height. From these data, we derived slope, ruggedness, orientation, and hill-shading variables. We applied spatial statistics to determine the spatial structure of the community. We computed Maxent ecological niche models to determine the importance of environmental variables. The community and its species presented a clustered distribution. We identified 14 clusters, composed of 1–3 species. Species records showed two distribution patterns, with clusters associated with steep and flat areas. Cluster outliers had the same patterns. Juveniles and subadults were associated with areas of low quality, while sexes used space in similar ways. Maxent models identified suitable habitats across the study area for two species and in the flat areas for the other two species. LiDAR allowed us to understand the local distributions of a lizard community. Remotely sensed data and LiDAR are giving new insights into the study of species ecology. Images of higher spatial resolutions are necessary to map important factors such as refuges.     

Modelling surface drainage patterns in altered landscapes using LiDAR

The detailed topographic information contained in light detection and ranging (LiDAR) digital elevation models (DEMs) can present significant challenges for modelling surface drainage patterns. These data frequently represent anthropogenic infrastructure, such as road embankments and drainage ditches. While LiDAR DEMs can improve estimates of catchment boundaries and surface flow paths, modelling efforts are often confounded difficulties associated with incomplete representation of infrastructure. The inability of DEMs to represent embankment underpasses (e.g. bridges, culverts) and the problems with existing automated techniques for dealing with these problems can lead to unsatisfactory results. This is often dealt with by manually modifying LiDAR DEMs to incorporate the effects of embankment underpasses. This paper presents a new DEM pre-processing algorithm for removing the artefact dams created by infrastructure in sites of embankment underpasses as well as enforcing flow along drainage ditches. The application of the new algorithm to a large LiDAR DEM of a site in Southwestern Ontario, Canada, demonstrated that the least-cost breaching method used by the algorithm could reliably enforce drainage pathways while minimizing the impact to the original DEM.     

Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network

Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods.     

Prediction and visualization of GPS multipath signals in urban areas using LiDAR Digital Surface Models and building footprints

This paper explains a ray tracing method which is applied to prediction and visualization of diffracted and reflected GPS signals in dense urban areas. Reflected and diffracted signals can have a detrimental effect on GPS positioning accuracy especially in highly built‐up areas. The ray tracing technique implemented in this paper is specially geared to LiDAR height pole data at 1‐m spatial resolution and 2D building footprints in raster and vector format, respectively. Such a simple data format allows for rapid implementation of 3D ray tracing in a GIS without further processing so that detailed 3D urban models in vector format are not required. Issues of spatial uncertainty in the data used are also addressed in relation to the identification of multipath signals. Some preliminary results obtained from fieldwork are presented and analysed in detail.     

Errors in LiDAR-derived shrub height and crown area on sloped terrain

This study developed and tested four methods for shrub height measurements with airborne LiDAR data in a semiarid shrub-steppe in southwestern Idaho, USA. Unique to this study was the focus of sagebrush height measurements on sloped terrain. The study also developed one of the first methods towards estimating crown area of sagebrush from LiDAR. Both sagebrush height and crown area were underestimated by LiDAR. Sagebrush height was estimated to within ± 0.26-0.32 mm (two standard deviations of standard error). Crown area was underestimated by a mean of 49%. Further, hillslope had a relatively low impact on sagebrush height and crown area estimation. From a management perspective, estimation of individual shrubs over large geographic areas can be accomplished using a 0.5 m rasterized vegetation height derivative from LiDAR. While the underestimation of crown area is substantial, we suggest that this underestimation would improve with higher LiDAR point density (>4 points/m²). Further studies can estimate shrub biomass using LiDAR height and crown area derivatives.     

Embedding user-generated content into oblique airborne photogrammetry-based 3D city model

Oblique airborne photogrammetry-based three-dimensional (3D) city model (OAP3D) provides a spatially continuous representation of urban landscapes that encompasses buildings, road networks, trees, bushes, water bodies, and topographic features. OAP3D is usually present in the form of a group of unclassified triangular meshes under a multi-resolution data structure. Modifying such a non-separable landscape constitutes a daunting task because manual mesh editing is normally required. In this paper, we present a systematic approach for easily embedding user-generated content into OAP3D. We reduce the complexity of OAP3D modification from a 3D mesh operation to a two-dimensional (2D) raster operation through the following workflow: (1) A region of interest (ROI) is selected to cover the area that is intended to be modified for accommodating user-defined content. (2) Spatial interpolation using a set of manually controlled elevation samples is employed to generate a user-defined digital surface model (DSM), which is used to reform the ROI surface. (3) User-generated objects, for example, artistically painted road textures, procedurally generated water effects, and manually created 3D building models, are overlaid onto the reformed ROI.