机载LiDAR点云中精细建筑物顶面的渐进提取

由LiDAR点云数据准确提取建筑物顶面是实现三维建筑模型自动重建的关键步骤.在分析现有顶面提取方法的基础上,提出一种渐进地提取LiDAR点云数据中精细建筑物顶面的方法.先以法向阈值和曲率阈值为约束,借助区域生长算法对原始点云进行初步分割,并得到面积较大、边界特征较明显的初始顶面;再借助主元分析法估算每个初始顶面的平面方程,并以点到平面的距离为约束,利用基于距离的区域生长算法提取其对应的精确顶面;最后通过随机抽样一致性算法(RANSAC)迭代地提取剩余点云中的小顶面.实验表明,通过动态调整阈值和迭代步骤,能够从LiDAR数据中精确地提取出复杂建筑物的顶面.     

一种改进的基于上下文信息的多源数据融合目标提取方法

拟提出一种综合利用航空影像与LiDAR数据提取山地滚石信息新方法:首先对影像进行多尺度分割得到分割对象,同时对LiDAR数据进行分类、插值和差分等处理获取高程信息。然后依据影像分割对象计算基于可见光波段的差异植被指数并用于去除植被信息的干扰,得到非植被影像分割对象。为了有效利用阴影,针对航空影像提出归一化差异阴影指数并根据阈值分割得到阴影对象。然后利用提出的基于阴影和高程的滚石信息自动提取算法对获取的非植被影像分割对象进行滚石初步提取,再根据实际需求设定高程阈值过滤得到最终的滚石信息。最后,以香港某地区的航空影像和LiDAR数据为基础,对提出的方法进行实例验证,结果表明该方法能够较好地提取滚石并有效地区分裸露基岩、道路等与滚石光谱信息相近的地物,滚石提取精度达到88%以上,基本能满足地政部门滚石防护的需求。     

建筑物边界约束的航空影像DSM优化

针对航空影像密集匹配生成点云数据边界模糊的问题,提出了一种基于DSM灰度影像矢量边界与DEM无约束D-三角网嵌套生成具有精确边界的建筑物表面模型的方法。通过逐点内插法建立实验区点云数据的DSM深度影像图;根据计算机视觉中的边缘检测算子,提取深度影像中建筑物的准确边界;建立DEM的无约束D-三角网,将准确建筑物边界作为硬边界嵌入三角网中,最终将建筑物三角网和地面点三角网拼合,生成"纯净"建筑物表面模型。实验结果表明,优化后的建筑物高度和平面信息无精度损失,该方法有较强实用性。     

基于机载LiDAR和高分辨率遥感影像的城市道路网提取

利用单个数据源的数学形态学道路提取方法不能充分利用道路的特征,提取的道路信息不完整。针对这一缺陷,并考虑到机载LiDAR数据可以提供高程信息,提出了将机载LiDAR数据和高分辨率遥感影像数据结合起来的城市道路网的提取方法。以徐州市的机载LiDAR数据和高分辨率遥感影像数据作为实验数据,首先将两者进行精确配准,然后利用伪道路信息去除的方法分别将植被信息和建筑物信息等去除,得到基本的道路轮廓,再利用形态细化算法提取道路的中心线,最后,在ArcGIS和Matlab编程环境下实现了改进的道路修剪算法(IRT),利用该算法进行道路修剪,得到了平滑和连贯的城市道路网。经过精度评价可以看出:利用该方法可以较好地避免建筑物阴影、低矮植被群等对道路提取的影响,道路的识别精度达到84%以上。     

视差互信息引导下的立体航空影像与LiDAR点云自动配准

目的 从视差图反映影像景物深度变化并与LiDAR系统距离量测信息"同源"这一认识出发,提出一种基于视差互信息的立体航空影像与LiDAR点云自动配准方法.方法 本文方法分为3个阶段:第一、通过半全局匹配SGM(semi-gdabal matching)生成立体航空影像密集视差图;第二、利用航空影像内参数及初始配准参数(外方位元素)对LiDAR点云进行"针孔"透视成像,生成与待配准的立体航空影像空间分辨率、几何形变相接近且具有相同幅面大小的模拟灰度影像-LiDAR深度影像,以互信息作为相似性测度依据估计航空影像视差图与LiDAR深度影像的几何映射关系,进而以之为基础实现LiDAR点云影像概略相关;第三、以LiDAR点云影像概略相关获得的近似同名像点为观测值,以视差互信息为权重,实施摄影测量空间后方交会计算获得优化的影像外方位元素,生成新的LiDAR深度影像并重复上述过程,直至满足给定的迭代计算条件.结果 选取重叠度约60%、幅面大小7 216×5 428像素、空间分辨率约0.5 m的立体航空像对与平均点间距约1.5 m、水平精度约25 cm的LiDAR"点云"进行空间配准实验,配准精度接近1个像素.结论 实验结果表明,本文方法自动化程度高且配准精度适中,理论上适用于不同场景类型、相机内参数已知立体航空影像,具有良好的应用价值.     

从LiDAR数据中提取建筑物平面目标的新方法

提出一种从机载LiDAR点云数据中自动提取建筑物平面的方法。给出了基于边长约束的三角形生长算法对建筑物初始区域进行提取,针对提取出的建筑物脚点,利用自适应Mean Shift方法在特征空间中对其进行聚类分析,并提取出平面目标,最后利用Alpha-Shape算法生成建筑物平面的轮廓线。通过实验证实了方法的有效性。     

基于机载LiDAR和多光谱图像的建筑物震害自动识别方法

地震破坏的建筑物在遥感影像上和空间上表现出的特征各异,致使遥感定量化估计其破坏程度较困难.本文介绍了基于LiDAR和多光谱影像相结合的多源遥感影像进行倒塌建筑物的面向对象识别的方法、分析处理步骤和特征参数选择,并以2010年1月12日海地地震后的太子港局部LiDAR数据和高分辨率卫星影像为例,提取了倒塌和未倒塌建筑物,经与高分影像目视解译结果比较,面向对象分类结果具有较高的分类精度.     

结合OTSU与迭代三角网的机载LiDAR建筑物点云提取

针对机载LiDAR建筑物点云提取过程中受地形影响参数设置困难,建筑物、树木难以区分等问题,提出一种结合最大类间方差法与迭代三角网相结合的机载LiDAR建筑物点云提取算法。在已有滤波结果的基础上,首先采用最大类间方差法对滤波得到的非地面点进行预处理,提取初始建筑物点;然后运用改进的迭代三角网方法对初始建筑物点云进行精确提取,得到最终的建筑物点云。实验选取国际摄影测量与遥感协会提供的三组LiDAR点云数据进行建筑物点云提取。结果表明,该算法可以较好地实现建筑物点云的高精度自动提取,且对不同屋顶类型以及地形具有良好的自适应性,验证了算法的可靠性。     

基于角点检测的建筑物轮廓矢量化方法

针对高分辨率航空影像的特点,提出了基于角点检测的建筑物轮廓矢量化方法:首先,对航空影像进行多尺度面积形态学分割,获取建筑物的二值图像;其次,利用边缘跟踪技术,记录边缘点,在边界曲线上利用高斯函数计算其曲率,提取候选角点集,并由自适应支持区域确定的角点角度和一个动态曲率阔值代替固定的阈值筛选出正确角点;最后,以直角作为约...     

机载LiDAR高差和高分影像的城市树冠提取比较

在机载LiDAR(Light Detection and Ranging)数据和高空间分辨率航空影像的支持下,以城市为实验区,实现了单木树冠提取。首先通过LiDAR数据获取高差模型,将其作为包含林木的感兴趣区,再通过掩膜方式提取高分影像上的相同区域,然后采用标记分水岭分割算法分别对两幅感兴趣区影像进行树冠提取,最后以人工勾绘树冠结果为参考评价分割精度,比较了两种数据源提取树冠的优缺点。结果显示,利用LiDAR数据获取的高差模型中包含85.28%的林木信息,林木区域提取的效果显著;基于高分影像得到的分割结果较好,F值为57.14%,基于高度差值模型影像的分割结果较差,F值为42.47%。表明分水岭算法方便可行,且高分影像提供的二维信息更适用于树冠提取。     

Performance evaluation of classification trees for building detection from aerial images and LiDAR data: a comparison of classification trees models

This study assesses the performance of three classification trees (CT) models (entropy, gain ratio and gini) for building detection by the fusion of airborne laser scanner data and multispectral aerial images. Data from four study areas with different sensors and scene characteristics were used to assess the performance of the models. The process of performance evaluation is based on four criteria: model validation and testing, classification accuracies, relative importance of input variables, as well as transferability of CT derived from one data set to another. The LiDAR point clouds were filtered to generate a digital terrain model (DTM) based on the orthogonal polynomials, and then a digital surface model (DSM) and the normalized digital surface model (nDSM) were generated. A set of 26 uncorrelated feature attributes were derived from the original aerial images, LiDAR intensity image, DSM and nDSM. Finally, the three CT models were used to classify buildings, trees, roads and ground from aerial images, LiDAR data and the generated attributes, with the most accurate average classifications of 95% being achieved. The entropy splitting algorithm proved to be a preferable algorithm for building detection from aerial images and LiDAR data.     

Photogrammetric processing of high-resolution airborne and satellite linear array stereo images for mapping applications

This article introduces a mathematical model for photogrammetric processing of linear array stereo images acquired by high-resolution satellite imaging systems such as IKONOS. The experimental result of the generation of simulated IKONOS stereo images based on photogrammetric principles, IKONOS imaging geometry and a set of georeferenced aerial images is presented. An accuracy analysis of ground points derived from the simulated IKONOS stereo images is performed. The impact of the number of GCPs (ground control points), distribution of GCPs, and image measurement errors on the ground point accuracy is investigated. It is concluded that an accuracy of ground coordinates from 2 m to 3 m is attainable with GCPs, and 5 m to 12 m without GCPs. Two data sets of HRSC (high resolution stereo camera) and MOMS (modular opto-electronic multispectral stereo-scanner)-2P are also utilized to test the model and system. The presented data processing method is a key to the generation of mapping products such as digital terrain models (DEM) and digitial shorelines from high-resolution satellite images.     

Line matching of wide baseline images in an affine projection space

ABSTRACT

Line matching plays an important role in vision localization and three-dimensional reconstruction of building structures. The conventional method of line matching is not effective for processing stereo images with wide baselines and large viewing angles. This paper proposes a line matching method in an affine projection space, aiming to solve the problem of change of viewing angles in aerial oblique images. Firstly, monocular image orientation can be performed through geometric structures of buildings. Secondly, according to the pose information of the camera, the affine projection matrix is obtained. The original image can be rectified as a conformal image based on this projection matrix, thereby reducing the difference in the viewing angle between images. Then, line matching is performed on the rectified images to get the matched line pairs. Finally, the inverse affine projection matrix is used to back-project the matched line pairs to the original images. The experimental results of five groups of aerial oblique images show that the matched line segments obtained by the proposed method are basically superior to those of the methods which are directly processed on the original image in terms of quantity, correctness, and efficiency.     

基于动态规划的城市道路提取改进算法

从航空或卫星影像中提取道路一直是研究的热点,基于动态规划的道路提取算法是最有效的算法之一。该文基于LiDAR点云数据特征改进了该算法的代价函数,进而提高了基于动态规划的道路提取算法的鲁棒性。为正确地融合航拍图像和LiDAR点云数据,研究了航拍图像和LiDAR点云数据的匹配算法。最后,通过试验验证了算法的正确性。     

True orthoimage generation in urban areas with very tall buildings

The orthoimage usually serves as a valuable base layer in GIS. With an increasing demand in many urban GIS applications, orthoimages in urban areas are required to represent spatial objects in their true positions. However, the traditional methods for orthoimage generation did not consider features (e.g. occlusion, shadow, etc.) of spatial objects (e.g. bridges and buildings), resulting in that spatial objects in the created orthoimages cannot be located in their true positions. This paper presents our research and experimental results of true orthoimage generation in extremely tall urban areas using lidar and multi-view large-scale aerial images. Lidar data are used for the extraction of an urban digital surface model (DSM), further for the extraction of a digital building model (DBM) and a digital terrain model (DTM). Data structure and a data model for managing urban spatial objects, such as buildings and bridges, are developed. The photogrammetric geometry is used for the detection of occluded and shadowed areas in true orthoimage generation. For the occluded and shadowed areas, lost information is compensated from a conjugate area in adjacent images, for which a new mosaicking method, which automatically chooses the 'best' imagery and automatically optimizes the seam line, has been developed. Experimental results from central Denver, Colorado and Lower Manhattan, New York City demonstrated that the proposed true orthoimage generation scheme in this paper is capable of truly orthorectifying the relief displacement in aerial images and significantly reducing occlusion and shadow defects.     

一种应用机载LiDAR数据和高分辨率遥感影像提取城市绿地信息的方法

提出了一种新的面向对象的城市绿地信息两阶段提取方法。该方法分阶段使用高分辨率遥感影像的光谱和2维形态信息以及机载LiDAR数据的3维形态信息作为分类依据。第1阶段,影像首先被分割为对象,对象被分类为无阴影的植被、阴影下的植被、水体、建筑物、空地和阴影6类地物;无阴影的植被和阴影下的植被合并为城市绿地对象,在第2阶段,将LiDAR数据产生的归一化数字表面模型nDSM与绿地对象叠加,计算每个对象的3维形态属性,进一步将绿地对象细分为草坪、灌木和乔木。以美国休斯敦中心城区为例,介绍了方法流程。精度分析表明,绿地的分类精度达到9346%;方法中的主要误差来源于遥感影像当中的建筑物阴影以及生成数字地形模型时所产生的误差。     

Optimization of DTM Interpolation Using SFS with Single Satellite Imagery

Digital terrain models (DTMs) in the present context are simply regular grids of elevation measurements over the land surface. DTMs are mainly extracted by applying the technique of stereo measurements to images available from aerial photography and/or remote sensing. Enormous amounts of local and global DTM data with different specifications are now available. However, numerous geoscience and engineering applications need denser and more accurate DTM data. Collecting additional height data in the field, if not impossible, is either expensive or time consuming or both. Stereo aerial or satellite imagery is often unavailable and very expensive to acquire. Interpolation techniques are fast and cheap, but have their own inherent difficulties and problems, especially in rough terrain. Advanced space technology has provided much single (if not stereo) high-resolution satellite images almost worldwide. Besides, shape from shading (SFS) is one of the methods to derive the geometric information about the objects from the analysis of the monocular images. This paper discusses the idea of using the SFS method with single high resolution imagery to optimize the interpolation techniques used in densifying regular grids of heights. Three different methodologies are briefly explained and then implemented with both simulated and real data. Numerical results are briefly discussed and a short discussion on how to make the computations more efficient will be presented. The implemented algorithms show that one can easily take advantage of parallel processing techniques to speed up the highly demanding computations involved in this application. Finally, a few remarks and conclusions are drawn.     

Multi-type change detection of building models by integrating spatial and spectral information

Detecting building changes followed by updates is preferable for efficient revisions to building models. Additionally, more change types can be detected with spatial information provided by building models for reducing land surveying work. Therefore, for efficient building of model revision and land surveys, this work applies a new multi-type change detection scheme with new light detection and ranging (LIDAR) point clouds, new aerial images and existing building models. By integrating the spatial information from LIDAR data and image-based spectral information, this work identifies changes to existing buildings and identifies newly built and changed buildings. To provide an initial value for further revisions, new building regions are generated from change detection results. Experimental results demonstrate that the proposed scheme has high accuracy for both change type determination and building region generation. To provide comprehensive observations, experimental results deemed unreliable are scrutinized.     

无人机LiDAR 场地勘测及BIM 规划设计研究与实践

基于无人机激光雷达航测获取地面几何模型和影像信息具有快速、高效和高精度 的特点,而且其数字化成果具备向建筑信息模型(BIM)平台迁移的优势,使用无人机激光雷达 设备开展了测图全流程实验,并分析了航测数据精度,探索了利用成果数据进行BIM 规划设计 应用的可行性及意义。研究成果验证了无人机LiDAR 测绘数据作为BIM 前期工程项目数据的 可行性,提升了BIM 设计的效率,拓展了工程全生命周期数据链形式。