机载LiDAR地形特征信息快速提取     

刘智敏  杨安秀  宿殿鹏  刘盼 《大地测量与地球动力学》2018,38(11):1186-1190

为更有效地获取地形特征信息，提出一种机载LiDAR地形特征信息快速提取算法。首先，通过构建二次曲面拟合模型，建立实测LiDAR地形数据与拟合曲面的几何规则；然后，采用LM算法迭代参数寻优，获得最优化结果下的地形拟合参数，计算拟合时间及拟合精度；最后，以地形拟合模型为基础，进行地形特征信息的快速提取。通过机载LiDAR实测数据验证，当最优搜索半径为2 m时，地形曲面的拟合时间仅为0.02 s，RMSE仅为5.09 cm。该算法保证了地形特征信息提取效率和精度，能够有效满足机载LiDAR科学研究和工程应用的技术需求。  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

基于深度残差网络的机载LiDAR点云分类     

赵传  郭海涛  卢俊  余东行  张保明 《测绘学报》2020,49(2):202-213

机载LiDAR点云的分类是利用其进行城市场景三维重建的关键步骤之一。为充分利用现有的图像领域性能较好的深度学习网络模型,提高点云分类精度,并降低训练时间和对训练样本数量的要求,本文提出一种基于深度残差网络的机载LiDAR点云分类方法。首先提取归一化高程、表面变化率、强度和归一化植被指数4种具有较高区分度的点云低层次特征;然后通过设置不同的邻域大小和视角,利用所提出的点云特征图生成策略,得到多尺度和多视角点云特征图;再将点云特征图输入到预训练的深度残差网络,提取多尺度和多视角深层次特征;最后构建并训练神经网络分类器,利用训练的模型对待分类点云进行预测,经后处理得到分类结果。利用ISPRS三维语义标记竞赛的公开标准数据集进行试验,结果表明,本文方法可有效区分建筑物、地面、车辆等8类地物,分类结果的总体精度为87.1%,可为城市场景三维重建提供可靠的信息。  相似文献   

无人机机载LiDAR在沿海滩涂大比例尺地形测绘中的应用     

郭春海  张英明  丁忠明 《测绘通报》2019,(9):155-158

以南京市以下长江12.5 m深水航道建设工程中的白茆沙整治建筑物工程的年度监测为例，介绍了利用无人机航摄和机载LiDAR技术进行大比例尺高精度的滩涂地理信息数据采集的关键技术。研究案例表明，采用无人机搭载数码相机和LiDAR联合进行数据采集，在小区域、通行困难地区的高分辨率影像和地面高程数据快速获取方面具有明显优势，为沿海滩涂开发中的工程用图测绘工作提供了一种新的有效快捷的技术手段。  相似文献   

Modelling forest canopy gaps using LiDAR-derived variables   总被引：1，自引：0，他引：1  

Leighton Lombard  Riyad Ismail 《国际地球制图》2019,34(2):179-193

Remote sensing has revolutionized forest management and has been widely employed to model canopy gaps. In this study, a canopy height model (CHM) and an intensity raster (IR) derived from light detection and ranging (LiDAR) data were used to model canopy gaps within a four-year-old Eucalyptus grandis forest using an object-based image analysis (OBIA) approach. Model thematic accuracies using the CHM, intensity raster and combined data set (CHM and IR) were all above 90%, with KHAT values ranging from 0.88 to 0.96. Independent test thematic accuracies were also above 90%, with KHAT values ranging from 0.82 to 0.91. A comparative area-based assessment yielded accuracies ranging from 70 to 90%, with the highest accuracies achieved using the combined data set. The results of this study show that using a CHM and intensity raster, and an OBIA approach, provides a viable framework to accurately detect and delineate canopy gaps within a commercial forest environment.  相似文献   

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways     

Suliman A. Gargoum  Karim El Basyouny 《地理信息系统科学与遥感》2019,56(6):864-893

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.  相似文献   

多尺度邻域特征下的机载LiDAR点云电力线分类   总被引：1，自引：0，他引：1  

王艳军  李凯  路立娟 《测绘通报》2019,(4):21-25

利用机载激光雷达技术三维测量精度高且获取快速的优点进行电力线自动分类提取已成为点云数据处理与电力应用的重要领域。针对电力线分类模型的自动化和高精度需求，本文提出了基于三维多尺度邻域特征的机载LiDAR点云电力线分类提取模型框架，主要包括4个步骤：电力线候选点滤波、多尺度邻域类型选取、形状结构特征提取和支持向量机分类。通过对2个复杂城市区域的试验数据集和8种不同邻域类型的详细结果对比分析，发现基于多尺度圆球邻域形状结构特征的分类模型结果准确率、召回率和质量分别达到97%、94%和93%，同时整体处理时间在2个试验数据中分别从366、256 s减少到274、160 s。试验结果表明，该方法在多种复杂城市场景中能够实现机载LiDAR数据的电力线较高精度分类提取。  相似文献   

真三维立体显示技术在不动产登记统一平台中的应用研究     

陈育新 《测绘与空间地理信息》2016,(5):107-109

随着国家不动产统一登记制度的实施,城市房屋、林地、草原、土地等不动产统一整合、调查和管理工作迫在眉睫。基于机载多角度倾斜摄影和激光雷达扫描技术的创新,真三维数据可为不动产登记提供真实统一、高精度、多层次的空间载体,保证了城市不动产登记应用创新和制度创新。  相似文献   

国产机载LiDAR系统安置角误差检校方案研究   总被引：1，自引：0，他引：1  

谢劭峰  刘田龙 《测绘与空间地理信息》2016,(6):50-55

机载激光扫描仪(Light Detection And Ranging,LiDAR)系统是由多个子系统集成,其中,安置角误差是集成误差中最大的误差源,安置角误差检校的方法多种多样,高效率、高精度的检校方式还需要试验的支撑。本文对平差模型法和几何模型法进行了试验分析,试验结果很好地证明了不同方法的优越性,为机载LiDAR系统的安置角检校提供了参考。  相似文献   

Pes shape variation in an ornithopod dinosaur trackway (Lower Cretaceous,NW Spain): New evidence of an antalgic gait in the fossil track record     

《Cretaceous Research》2016

Trackways can provide unique insight to animals locomotion through quantitative analysis of variation in track morphology. Long trackways additionally permit the study of trackmaker foot anatomy, providing more insight on limb kinematics. In this paper we have restudied the extensive tracksite at Barranco de La Canal-1 (Lower Cretaceous, La Rioja, NW Spain) focussing on a 25-m-long dinosaur (ornithopod) trackway that was noted by an earlier study (Casanovas et al., 1995; Pérez-Lorente, 2003) to display an irregular pace pattern. This asymmetric gait has been quantified and photogrammetric models undertaken for each track, thus revealing distinct differences between the right and the left tracks, particularly in the relative position of the lateral digits II–IV with respect to the central digit III. Given that the substrate at this site is homogenous, the consistent repetition of the collected morphological data suggests that differences recorded between the right and the left tracks can be linked to the foot anatomy, but more interestingly, to an injury or pathology on left digit II. We suggest that the abnormal condition registered in digit II impression of the left pes can be linked to the statistically significant limping behaviour of the trackmaker. Furthermore, the abnormal condition registered did not affect the dinosaur's speed.  相似文献