Multi-scale solution for building extraction from LiDAR and image data

This paper presents a multi-scale solution based on mathematical morphology for extracting the building features from remotely sensed elevation and spectral data. Elevation data are used as the primary data to delineate the structural information and are firstly represented on a morphological scale-space. The behaviors of elevation clusters across the scale-space are the cues for feature extraction. As a result, a complex structure can be extracted as a multi-part object in which each part is represented on a scale depending on its size. The building footprint is represented by the boundary of the largest part. Other object attributes include the area, height or number of stories. The spectral data is used as an additional source to remove vegetation and possibly classify the building roof material. Finally, the results can be stored in a multi-scale database introduced in this paper. The proposed solution is demonstrated using the data derived from a Light Detection And Ranging (LiDAR) surveying flight over Tokyo, Japan. The results show a reasonable match with reference data and prove the capability of the proposed approach in accommodation of diverse building shapes. Higher density LiDAR is expected to produce better accuracy in extraction, and more spectral sources are necessary for further classification of building roof material. It is also recommended that parallel processing should be implemented to reduce the computation time.     

一种基于形态学的激光雷达数据滤波算法的改进

机载激光雷达是DEM生产的重要技术手段之一.为了获取高分辨率的DEM,首先要将建筑物、车辆、植被等非地面点去除,这一过程称为滤波.本文在现有算法的基础上提出一种改进的形态学滤波算法.该算法逐步增大滤波窗口尺寸,并采用自适应的高差阈值,较好地保留了地形拓扑信息.试验采用ISPRS提供的参考数据进行测试分析,该数据代表典型...     

张量投票的机载LiDAR数据建筑物自动提取

针对机载LiDAR目标提取中建筑物与树木难以有效区分的问题,该文提出了一种基于坡度自适应穿透率和张量投票的建筑物检测方法。在利用滤波和高差阈值去除地面点和矮小地物点的基础上,采用坡度自适应穿透率作为区分建筑物和植被的主要特征,较好地突出了建筑物点和树木点在空间分布上的密度差异;设计基于张量投票的投票算法,对坡度自适应穿透率特征进行邻域投票,以促进相邻点之间特征信息的传递,增强了该特征对于植被和建筑物的可分性。采用ISPRS提供的测试数据进行实验,结果表明,该方法能有效地区分建筑物和树木点,提取的建筑物完整度达94.6%,准确度达98.3%。     

融合LiDAR数据与航空影像的面向对象水体提取

针对单一数据源难以快速、准确提取水体信息的问题,提出一种利用面向对象技术有效融合LiDAR点云与影像数据,准确提取水体信息的新方法。采用分形网络演化算法对航空影像进行多尺度分割得到影像对象,提出对象平均回波强度概念,利用LiDAR回波强度信息计算OAI,选取对象面积和OAI构建特征空间提取水体。实验结果表明,该方法简单高效,水体提取效果良好。该文方法可为无需精确配准的高精度水体提取、利用面向对象技术处理基于LiDAR数据和航空影像的信息提取等问题提供参考。     

基于机载LiDAR数据的建筑物轮廓提取

建筑物轮廓作为建筑物三维重建的重要元素,在建立智慧城市和数字城市中至关重要。本文针对从机载激光雷达点云中提取建筑物轮廓数据处理的点云滤波、建筑物屋顶面提取、建筑物轮廓提取,以及提取精度评定各环节存在的一些问题,提出了一种综合区域生长改进算法、三维Hough变换算法和α-shape算法的建筑物轮廓提取方法。该方法在对机载LiDAR点云数据去噪的基础上,首先利用改进的区域生长算法滤波地面点,并基于地物点到地面的归一化高程特征通过高度阈值去除高度较为低矮的地物点;再基于三维Hough变换算法从剩余建筑物和高大树木点云中提取建筑物平面;最后使用α-shape算法提取建筑物的轮廓信息。对使用RIEGLVQ-1560i机载激光雷达测量系统扫描的某城区点云数据进行计算,通过匹配度、形状相似度和位置精度等评价指标对提取的建筑物轮廓进行精度评定。结果表明,综合区域生长改进算法、三维Hough变换算法和α-shape算法的建筑物轮廓提取方法可以准确提取建筑物的轮廓信息,对于大范围的建筑物轮廓提取具有稳定性和普遍适用性。     

基于语义特征的堤防外坡激光脚点分割

在缺乏影像等辅助数据的情况下,本文直接利用激光雷达LiDAR点云数据,提取水系边缘,并在此基础上实现基于语义分割的堤防外坡激光点云提取方法。分两步进行边坡的提取:首先获取LiDAR数据中水体边缘轮廓线,进一步获取堤防边坡的下缘线;然后以下缘线为增长基线,通过最小二乘平面拟合,利用区域增长的方法,将边坡平面提取出来,从而获取边坡脚点。     

一种基于机载LiDAR点云的林间道路提取方法

基于林间道路的形态特征和支持向量机原理(SVM),提出一种从机载LiDAR点云数据中提取林间道路的方法。首先,选取末次回波去噪、栅格化,生成数字表面模型(DSM)和强度信息模型(DIM);然后,通过支持向量机对坡度信息进行分类,得到道路潜在区域;再对道路潜在区域进行强度信息的分类,得到含有少量噪声的初始道路区域;最后,利用形态参数对初始道路区域进行去噪、精化,得出最终道路区域。实验证明,该方法能较好地提取出道路区域。     

利用原始激光雷达点云数据提取渐进式建筑物轮廓线

建筑物是城市三维建模的重要元素,其轮廓信息的提取既是难点又是重点。本文提出了原始激光雷达点云数据的渐进式建筑物轮廓线提取方法。首先对原始点云数据采用渐进数学形态学滤波分离非地面点;然后使用改进的三维Hough转换分类出建筑物点云;进一步提取建筑物轮廓点,并根据相邻点方位角阈值确定建筑点云轮廓的关键点,以此简化并拟合建筑物轮廓线;最后基于轮廓线长度加权方向将建筑物轮廓规则化。结果表明,该方法大大提高了点云处理的效率和精度,可以直接从采集到的初始数据中自动化渐进式得到建筑物轮廓线信息。同时该方法对解决中小城镇建筑物体积小,距离近和屋顶坡度较大等问题具有较好的效果。     

Accuracy test of point-based and object-based urban building feature classification and extraction applying airborne LiDAR data

Point-based and object-based building extractions were conducted in airborne LiDAR data in a sample area of Buffalo, New York. First, the earth surface points were filtered from the entire laser scan data set using a new filtering algorithm, which combines the TIN slope modelling and statistical analysis. The off-ground points were extracted for buildings in the study area using both point cluster analysis and object-oriented classifications. The accuracies of both approaches were tested using the digitised ground truth. The outcomes of accuracy testing of the point-based method are correctness: 88.74%, completeness: 92.67% and quality: 83.50%. The results of the accuracy of object-based building extraction are correctness: 87.21%, completeness: 60.14%, and quality: 55.26%. Reconstructions of 3D building models based on the extracted building points were performed. This study contributes scientific and technological knowledge for researchers in developing more effective methods in converting the LiDAR survey to a 3D GIS database.     

机载LiDAR点云中道路的提取方法

针对国内外许多专家学者对道路提取进行了大量的研究,但国内少有文章对这些研究进行归纳总结这一问题,该文对近年来国内外所有的研究现状进行总结概括。机载LiDAR点云进行道路提取中由于道路点云与地面点云高程相差很小、激光反射强度相近,导致道路提取一直是一个难点。文中系统阐述了从LiDAR中进行道路提取的各个过程、存在的问题以及对应的处理方法。最后,对此项研究内容进行了展望。     

一种机载激光雷达道路点云的半自动提取方法

针对直接从LiDAR点云数据中提取道路信息比较困难的问题,文章提出了一种基于点云分割和区域生长的机载LiDAR数据道路点云提取方法:采用曲面生长法对点云进行分割,直接得到包含道路信息的曲面点集合;应用LiDAR数据的回波强度对分割结果中的道路进行强度标定,并采用区域增长的思想实现了道路的精细提取。实验表明,该方法能够高效、准确地提取道路点云,在路桥建模方面有较强的使用价值。     

空间域分割的机载LiDAR数据输电线快速提取

机载LiDAR具有快速、直接获取地物3维坐标的能力,在电网高压输电线路安全巡检中具有较大的应用前景。论文针对机载LiDAR输电线智能巡检的需求,提出并实现了一种基于空间域分割的LiDAR点云数据输电线自动提取方法。该方法首先利用高程直方图统计法去除地面点,再次利用点云密度差异剔除杆塔,根据相邻线之间的距离差和相邻层的高程差进行单根输电线分离。最后,采用多项式模型在3维空间中重构每根输电线空间坐标。实验结果表明该方法能够快速自动地提取多个杆塔之间的多根输电线数据,具有一定的工程应用价值。     

Effect of data density,scan angle,and flying height on the accuracy of building extraction using LiDAR data

A Hough transform based approach for extraction of buildings using LiDAR data is presented. It is argued that LiDAR data should be smoothed and sparsed prior to Hough transform for better result. Algorithms to realize this are presented. Further, an algorithm which fits a vector model to extracted buildings is outlined. Simulated LiDAR data have been used to investigate the effect of three parameters (data density, flying height, and scan angle) on the quality of buildings extracted. A set of accuracy indices is proposed for this purpose. It is shown that the data density is the most significant parameter affecting the accuracy of building identification.     

基于LIDAR回波信息的道路提取

机载激光扫描系统在采集三维坐标的同时,也提供回波信息(回波次数、回波强度).本文提出了一种利用回波信息提取道路的方法:逐层加密,TIN提取数字地形模型(DTM),根据点云的回波信息从DTM中提取道路信息,通过搜索孤立点的滤波算法删除其中的噪声点.最后用实测数据对提取的道路信息进行精度分析,验证了该提取方法的有效性.     

无人车平台激光点云中线特征提取

随着无人平台的应用越来越广泛,由激光点云提取线特征构建高精度特征地图已成为研究的重点。本文基于深度图像中的二维线特征,提出了一种新的几何模型对其进行优化,得到准确的三维线特征,并使用贝叶斯滤波对多帧结果进行融合,提高了三维再线特征的精度和准确率。     

机载激光雷达数据中电力线的快速提取

针对当前机载LiDAR技术在电力巡线应用中对电力线数字模型高精度和快速重建的需求,该文提出一种高效的电力线点云分类方法。首先基于局部范围点的高程统计直方图,实现电力线点的快速粗提取;然后运用随机抽样一致性算法剔除残留的电塔点,结合点云高程统计进一步剔除绝缘子点,实现电力线点的精提取;最后利用同一垂直面内电力线点的高程分布特性,实现单根电力线点的快速提取。基于实际输电线路机载LiDAR数据的实验结果表明,该方法可实现电力线点的快速、高精度提取:粗分类后的电力线点中仅含约10%的非电力线点;精分类后约有2%的电力线点被误分为绝缘子点,最终各条电力线点的提取比率平均为98%以上。     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

激光雷达数据的地面线的自动生成方法

针对公路勘察设计中获取地面线信息效率低、精度低的问题,提出一种新的利用海量高精度激光雷达数据生成设计地面线的方法。该方法采用基于路线方案的索引方法对激光雷达数据进行动态管理,并且结合Microstation平台二次开发,设计并实现了道路设计地面线自动提取的软件工具。通过工程实践表明:利用该方法可以进行设计地面线的快速自动提取,提高公路勘测设计的效率,节省外业测量成本,并实现激光雷达测量与公路施工图设计的一体化。     

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.     

融合高分影像和LiDAR数据的城市道路提取

为避免由于城市道路复杂及树木建筑的阴影遮挡导致从遥感影像中提取道路信息不准确的问题,本文采用高分影像和Li-DAR数据相融合的方法实现城市道路的提取,并使用一种基于最小面积外接矩形(MABR)的后处理改进方法进行完善.首先对试验区进行数据配准;然后应用FNEA算法进行图像分割,并使用随机森林分类法进行分类,将影像融合和...