Integration of high-resolution imagery and LiDAR data for object-based classification of urban area

This paper investigates the synergistic use of high-resolution multispectral imagery and Light Detection and Ranging (LiDAR) data for object-based classification of urban area. The main contribution of this paper is the development of a semi-automated object-based and rule-based classification method. In the implemented approach, the diverse knowledge about land use/land cover classes are transformed into a set of specialized rules. Further, this paper explores supervised Gaussian Mixture Models for classification, which have been primarily used for unsupervised classification. The work is carried out on test data from two different sites. Contribution of the LiDAR data resulted in a significant improvement of overall Kappa. Accuracy assessment carried out for aforementioned classification methods shows higher overall kappa for both the study sites.     

机载激光雷达数据的建筑物三维模型重建

机载激光雷达技术可以快速获取建筑物高精度高密度的三维空间信息.文章提出一种建筑物三维模型重建方法,该方法利用栅格数据使用RANSAC算法提取建筑边界上的长线段,采用规则化策略获取建筑轮廓;在模型生成阶段,基于原始点云和屋顶拓扑关系约束,获取屋顶间的交线和交点,并结合已得到的建筑边界、屋顶间交线和交点获取三维建筑模型.实验结果表明,该方法思路清晰、科学可行且适用性强,提取的建筑边界和模型垂直精度高.     

利用机载LiDAR点云提取复杂城市建筑物面域

机载LiDAR技术为探测建筑物提供了大量三维点云坐标.为了能从植被中有效识别建筑物面域,首先利用渐进式TIN加密法识别非地面点云,经过移除低于地面3 m的点云和孤立点云后生成菲地面点云的二值化格网,依据自定义的分割算子打断建筑物和植被间的可能连接;然后通过区域生成算法以高差阈值来聚类二者的面域,并使用大坡度密度阈值来提取建筑物的面域;最后使用形态学闭算子填充面域孔洞并平滑其边缘.选取3个典型的复杂城市区域进行测试,结果显示,各区域的提取质量与完成率均高于91％,表明该算法能够达到自动识别建筑物的目的.     

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。     

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。     

Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County,New Jersey

Extracting high-quality building footprints is a basic requirement in multiple sectors of town planning, disaster management, 3D visualization, etc. In the current study, we compare three different techniques for acquiring building footprints using (i) LiDAR, (ii) object-oriented classification (OOC) applied on high-resolution aerial photographs and (iii) digital surface models generated from interpolated LiDAR point cloud data. The three outputs were compared with a digitized sample of building polygons quantitatively by computing the errors of commission and omission, and qualitatively using statistical operations. These findings showed that building footprints derived from OOC gave highest regression and correlation values with least commission error. The R² and R values (0.86 and 0.92, respectively) imply that the footprint areas derived by OOC matched more closely with the actual area of buildings, while a low commission error of 24.7% represented a higher number of footprints as correctly classified.     

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...     

机载LiDAR建筑物点云渐进提取算法

针对机载LiDAR建筑物点云提取过程中易受植被的影响的问题,本文提出了一种机载LiDAR建筑物点云的渐进提取算法.首先通过布料模拟滤波算法对地面点云与非地面点云进行区分,在此基础上利用最大类间方差法算法(Otsu)对非地面点云进行阈值分割,提取初始建筑物点云;然后根据点云的连通性对初始建筑物点云进行密度聚类分割(DBS...     

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector-machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification.     

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。     

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%。     

密集城区高分辨率遥感影像建筑物提取

建筑物在地理国情监测中是一个重要目标,快速、准确地提取城市建筑物可以带来巨大的经济价值。本文在前人针对城市区域的建筑物提取研究基础上,对现有提取方法存在的问题,提出了一种针对密集城区的面向对象自动化建筑物提取流程。首先利用高分辨率遥感影像得到阴影和建筑物初提取结果;然后利用阴影和建筑物的空间位置关系,建立筛选条件,对疑似建筑物区域过滤;最后通过图割算法来精确建筑物轮廓。通过使用武汉地区的两幅QuickBird影像进行算法验证试验,可得到准确的检测结果。本算法可应用于密集城区的建筑物检测,能够有效减少人工判图的工作量。     

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m^?2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.     

车载LiDAR点云数据中杆状地物自动提取与分类

针对城市道路场景中车载LiDAR点云数据质量差、各类地物相互遮掩的情况,提出杆状地物自动提取与分类算法。先通过改进数学形态学算法移除点云数据中的地面点,再根据杆状地物的形态特征,使用纵向格网模板初步提取杆状地物,然后对提取的疑似杆状地物进行点云数据规则化并通过统计分析移除噪声点,最后根据预先建立的杆状地物样本训练SVM分类模型,对提取的杆状地物进行分类。试验表明,本文方法能够在数据质量欠佳的情况下有效提取城市道路场景中的杆状地物,并对提取的杆状地物进行高精度分类。     

顾及城区机载LiDAR数据特征的配准算法

条带配准是点云数据处理的一个重要环节.文章介绍了经典ICP配准算法的基本原理;考虑到城区机载LiDAR数据中地物的组成、空间分布等特征,提出了一种ICP算法中对应点集的选择策略,并建立了城区机载LiDAR数据的自动配准流程.利用河南安阳试验场的真实点云数据进行配准实验,结果表明在不需要人工干预的情况下,可以很好地将条带数据拼接在一起,建立无缝点云数据.     

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。     

基于分类与形态综合的高分辨率影像建筑物提取方法研究

本文提出了一种基于分类和形态综合的高分辨率影像建筑物提取方法。主要研究的是基于高分辨率影像多尺度分割的面向对象人工地物分类方法,并以此为基础提取房屋,实现房屋位置、范围的初步自动化提取。最后,对提取的地物图斑的精确边界利用Hough变换提取直线边缘轮廓线,并以此为基础进行轮廓拟合和制图综合,从而得到规则的、方形组合的建筑物形状信息。     

利用样本生成方法进行机载多光谱LiDAR数据深度学习分类

机载多光谱LiDAR系统能够快速、准确地获取地物的空间几何和光谱信息,为地物覆盖分类和目标识别提供新的数据源。近年来,基于三维点云的深度学习算法取得了一系列突破性进展,然而直接将不规则的原始点云数据输入深度学习模型进行基于点的分类存在一定的困难。本文提出了一种基于FPS-KNN的样本生成方法,用于基于深度学习的机载多光谱LiDAR数据分类。该方法首先对输入数据进行归一化处理;然后利用最远点采样方法(FPS)和K近邻法(KNN)在输入数据中生成一系列规则大小的训练样本数据集。通过机载多光谱LiDAR数据的试验表明,该方法所生成的样本不仅符合卷积神经网络所要求的输入数据形式,而且能够确保对输入场景的完整覆盖。     

Assessment of various parameters on 3D semantic object-based point cloud labelling on urban LiDAR dataset

Semantic labelling of LiDAR point cloud is critical for effective utilization of 3D points in numerous applications. 3D segmentation, incorporation of ancillary data, feature extraction and classification are the key stages in object-based point cloud labelling. The choice of algorithms and tuning parameters adopted in these stages has substantial impact on the quality of results from object-based point cloud labelling. This paper critically evaluates the performance of object-based point cloud labelling as a function of different 3D segmentation approaches, incorporation of spectral data and computational complexity of the point cloud. The designed experiments are implemented on the datasets provided by the ISPRS and the results are independently validated by the ISPRS. Results indicate that aggregation of dense point cloud into higher-level object analogue (e.g. supervoxels) before 3D segmentation stage offers superior labelling results and best computational performance compared to the popular surface growing-based approaches.     

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Li DAR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。