基于纵横断面的机载雷达点云数据滤波     

黄迟  张汉德  范学炜  纪红如 《测绘科学》2015,(11):34-38

针对现有基于坡度滤波的改进方法难以同时提升滤波速度与精度的问题,文章提出了一种基于纵横断面的机载LiDAR点云数据滤波方法。该方法视测区为多个断面构成,以行(列)为滤波基本单位,兼顾高程突变的方向信息,分别从纵横断面以基于坡度的判据进行滤波处理,可同时提高计算速度与结果精度。实验结果表明,该算法计算速度快,并且对于不同特征的地形都能取得较好的滤波效果。  相似文献   

基于平面特征的地面雷达点云配准算法     

张东  黄腾 《测绘科学》2015,(11):146-149

针对地面LiDAR点云配准中不同坐标系点云数据存在对应的平面特征不同的问题,文章提出了一种基于总体最小二乘的地面LiDAR点云数据配准算法:通过对分割后的点云数据平面拟合,得到相应法向量;根据不同坐标系中LiDAR点云数据对应的平面法向量,利用反对称矩阵和罗德里格矩阵的性质,用3个独立参数代替3个旋转参数,采用总体最小二乘法建立旋转矩阵解算模型;采用总体最小二乘法确定平移参数的计算公式;最后根据转换后特征点云与对应平面点云的重复情况,给出了配准模型的精度公式。实验结果表明该方法精度较高,可以取得较好的点云配准效果,适合于含有大量重复平面特征的点云数据的配准。  相似文献   

机载激光雷达技术和摄影测量匹配技术在DEM生产中的应用     

王继承 《测绘与空间地理信息》2015,(5)

机载激光雷达技术和摄影测量匹配技术是国内制作DEM最为常见的两种技术,本文对这两种技术的作业流程及优缺点进行了详细论述,并在实验中将这两种技术组合应用,最终证明这两种技术如果能恰当地组合使用,可在DEM生产中显著提高生产效率.  相似文献   

采用Morse理论的小尺度地形特征提取方法     

张春亢  赵学胜  王洪斌 《测绘科学技术学报》2015,(3):266-270

Li DAR点云为小尺度地表形态的提取与表达提供了精确的数据源,但其高密度性与不确定性,导致应用Morse理论提取的特征点中含有大量的"伪特征点"。这里首先通过定义特征点指数等一系列概念,模拟特征点周围区域的地表形态,建立特征点重要性度量指标与计算方法;然后给出了地表重要特征点的提取算法;最后,进行了试验验证与分析。结果表明:提出的算法优于现有的持续值法与自然法则法,可以有效剔除"伪特征点",实现基于Li DAR点云小尺度复杂地形的特征点精确提取与多层次表达。  相似文献   

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   

电力巡线LiDAR点云电塔自动定位和提取算法     

黄陆君  陈光平  袁帅  涂朴  乔杰 《测绘通报》2020,(4):53

针对电力巡线机载激光雷达（LiDAR）激光点云电塔自动提取问题，提出了一种电塔自动定位和点云提取算法。首先，基于点云进行二维空间网格划分，利用网格点云高程偏差和方差特征提取潜在电塔网格；其次，基于电塔点云的高程连续特性完成电塔自动定位和点云粗提取；然后，利用点云分层密度信息和图像开运算，实现电塔精细提取；最后，利用轻小型无人机载激光雷达数据验证本文算法的有效性。试验结果表明，本文所提出的自动提取算法，能够有效解决LiDAR数据中电塔自动定位和点云提取问题，在LiDAR数据质量较差时仍能够取得良好效果，算法对于噪点数据具有较强的稳健性。本文所提出的电塔自动提取算法在LiDAR电力巡检数据处理中具有一定的应用价值。  相似文献   

Terrestrial LiDAR monitoring of coastal foredune evolution in managed and unmanaged systems     

I. Conery  K. Brodie  N. Spore  J. Walsh 《地球表面变化过程与地形》2020,45(4):877-892

Coastal dunes provide essential protection for infrastructure in developed regions, acting as the first line of defence against ocean-side flooding. Quantifying dune erosion, growth and recovery from storms is critical from management, resiliency and engineering with nature perspectives. This study utilizes 22 months of high-resolution terrestrial LiDAR (Riegl VZ-2000) observations to investigate the impact of management, anthropogenic modifications and four named storms on dune morphological evolution along ~100 m of an open-coast, recently nourished beach in Nags Head, NC. The influences of specific management strategies – such as fencing and plantings – were evaluated by comparing these to the morphologic response at an unmanaged control site at the USACE Field Research Facility (FRF) in Duck, NC (33 km to the north), which experienced similar environmental forcings. Various beach-dune morphological parameters were extracted (e.g. backshore-dune volume) and compared with aeolian and hydrodynamic forcing metrics between each survey interval. The results show that LiDAR is a useful tool for quantifying complex dune evolution over fine spatial and temporal scales. Under similar forcings, the managed dune grew 1.7 times faster than the unmanaged dune, due to a larger sediment supply and enhanced capture through fencing, plantings and walkovers. These factors at the managed site contributed to the welding of the incipient dune to the primary foredune over a short period of less than a year, which has been observed to take up to decades in natural systems. Storm events caused alongshore variable dune erosion primarily to the incipient dune, yet also caused significant accretion, particularly along the crest at the managed site, resulting in net dune growth. Traditional empirical Bagnold equations correlated with observed trends of backshore-dune growth but overpredicted magnitudes. This is likely because these formulations do not encompass supply-limiting factors and erosional processes. © 2019 John Wiley & Sons, Ltd.  相似文献   

Impact of three-dimensional attitude variations of an unmanned aerial vehicle magnetometry system on magnetic data quality     

Callum A. Walter  A. Braun  G. Fotopoulos 《Geophysical Prospecting》2019,67(2):465-479

Optically pumped vapour magnetometers have an orientation dependency in measuring the scalar component of the ambient magnetic field which leads to challenges for integration with mobile platforms. Quantifying the three-dimensional attitude variations (yaw, pitch and roll) of an optically pumped vapour magnetometer, while in-flight and suspended underneath a rotary unmanned aerial vehicle, aids in the successful development of reliable, high-resolution unmanned aerial vehicle magnetometry surveys. This study investigates the in-flight three-dimensional attitude characteristics of a GEM Systems Inc. GSMP-35U potassium vapour magnetometer suspended 3 m underneath a Dà-Jiāng Innovations S900 multi-rotor unmanned aerial vehicle. A series of unmanned aerial vehicle-borne attitude surveys quantified the three-dimensional attitude variations that a simulated magnetometer payload experienced while freely (or semi-rigidly) suspended underneath the unmanned aerial vehicle in fair weather. Analysis of the compiled yaw, pitch and roll data resulted in the design of a specialized semi-rigid magnetometer mount, implemented to limit magnetometer rotation about the yaw axis. A subsequent unmanned aerial vehicle-borne magnetic survey applying this specialized mount resulted in more than 99% of gathered GSMP-35U magnetic data being within industry standards. Overall, this study validates that maintaining magnetometer attitude variations within quantified limits (±5° yaw, ±10° pitch and roll) during flight can yield reliable, continuous and high-resolution unmanned aerial vehicle-borne magnetic measurements.  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   

APPLICATION OF UAVLS TO RAPID GEOLOGICAL SURVEYS          下载免费PDF全文

SHAO Yan-xiu  ZHANG Bo  ZOU Xiao-bo  WANG Ai-guo  ZHANG Fan-yu  YUAN Dao-yang  LIU Xing-wang  HE Wen-gui 《地震地质》2017,39(6):1185-1197

Three-dimensional scanning with LiDAR has been widely used in geological surveys. The LiDAR with high accuracy is promoting geoscience quantification. And it will be much more convenient, efficient and useful when combining it with the Unmanned Aerial Vehicle (UAV). This study focuses on UAV-based Laser Scanning (UAVLS)geological field mapping, taking two examples to present advantages of the UAVLS in contrast with other mapping methods. For its usage in active fault mapping, we scanned the Nanpo village site on the Zhangxian segment of the West Qinling north-edge fault. It effectively removed the effects of buildings and vegetation, and uncovered the fault trace. We measured vertical offset of 1.3m on the terrace T₁ at the Zhang river. Moreover, we also scanned landslide features at the geological hazard observatory of Lanzhou University in the loess area. The scanning data can help understand how micro-topography affects activation of loess landslides. The UAVLS is time saving in the field, only spending about half an hour to scan each site. The amount of average points per meter is about 600, which can offer topography data with resolution of centimeter. The results of this study show that the UAVLS is expected to become a common, efficient and economic mapping tool.  相似文献