无控制条件下机载LiDAR航带拟稳平差方法

对无控制条件下机载LiDAR测区数据在平差过程中，由于受平差基准选取因素的影响，或难以补偿因POS系统导致的系统偏差，或容易造成误差累积的问题，提出了一种机载LiDAR航带拟稳平差方法。首先介绍了航带平差方法采用的航带形变模型，然后提出了航带拟稳平差模型，并推导了航带拟稳平差约束条件，同时可根据测区实际情况调整拟稳基准权值。最后利用该模型进行机载LiDAR航带平差。实验表明，该方法能够在无控制条件下有效减弱或消除机载LiDAR数据的系统误差，同时抑制航带平差过程中累积误差的产生，从而提高点云数据的质量和精度。     

机载LiDAR数据的LZD航带平差

系统误差是影响机载LiDAR系统精度的主要因素,因而消除或削弱系统误差影响、提高数据精度具有重要理论意义和工程实用价值。提出基于最小高程差(LZD)的机载LiDAR航带平差方法,并通过引入高斯-马尔柯夫模型提高了平差精度。实验部分考察了引入高斯-马尔柯夫模型的必要性及算法精度。实验结果表明:引入高斯-马尔柯夫模型可有效地提高算法精度;使用LZD进行机载LiDAR航带平差获取的结果均可以满足工程生产的精度需求;与商业软件TerraMatch相比,LZD的精度和TerraMatch的精度相当。     

一种去除机载LiDAR航带重叠区冗余点云的方法

机载LiDAR系统在获取高密度地表点云的同时,也带来了数据冗余的问题,特别是在航带重叠区中尤为突出。旨在研究无完整航迹信息辅助下去除航带重叠点,提出了基于点云GPS时间直方图的去除航带重叠点的方法。该方法包括三个步骤:(1)建立点云的GPS时间直方图,并根据GPS时间直方图特点获取航带重叠区外包矩形以及外包矩形中的所有点云;(2)考虑到城市中高密度点云有助于建筑物的三维重建,通过滤波分类处理获取建筑物点并予以全部保留;(3)对重叠区中除建筑物点外的其他所有点进行格网数据组织并根据GPS时间直方图逐格网去除航带冗余点。实验结果表明,该方法能较好地保留建筑物点的同时高效去除航带重叠点且不依赖于航迹信息,提高了后续数据分析处理的效率。     

机载大光斑激光雷达数据估测森林结构参数研究进展

针对LiDAR对森林结构具有较强探测能力,从而能够准确地获取森林的三维结构的优势,该文试图将该技术运用于估测森林结构参数,并介绍了机载大光斑LiDAR系统的工作原理和主要技术规格,系统总结了机载大光斑LiDAR数据估测森林结构参数的研究现状,分析了机载大光斑LiDAR估测森林结构参数的局限性并对其进行了展望。     

多航带LiDAR强度信号校正实验研究

LiDAR不仅能获取目标的三维信息,还能记录它的回波强度。针对强度信息在传播过程中受到外界因素的影响而含有较大误差的问题,从强度在传播过程中的衰减机理出发,提出一种多航带数据的强度自动校正算法。该算法基于雷达方程削减环境因素的影响提升强度信号精度,基于多航带重叠区的冗余数据自动筛选同名激光脚点,并针对同名样本的剩余强度差异,构建新的经验模型,消除航带间强度差异。实验结果表明,所提出的算法能够有效改进航带间强度差异并提高LiDAR强度数据的分类精度。     

一种高程相关的机载LiDAR点云配准方法

针对现有配准方法难以提取大范围机载LiDAR点云特征信息的问题,提出了一种基于2片待配准机载LiDAR点云高程数据相关的点云自动配准方法。首先,将待配准点一定范围内的点云拟合局部曲面;然后,在另一点云片中确定搜索区域,利用拟合结果求解搜索区域内的点云在拟合曲面上的高程;最后,通过计算拟合高程与实际高程的相关系数,选择搜索区域内相关系数最大位置作为配准的关键点参与点云配准,反复迭代直到完成配准。文章用实际采集的机载LiDAR数据进行了实验分析,并与传统的ICP算法进行了对比。实验结果表明,该方法在配准精度上能达到较高的水准,能够满足机载LiDAR点云配准的要求。     

基于数据流的TIN迭代滤波算法

通过机载LiDAR数据滤波获取地面信息是机载LiDAR数据的一项重要且基本的应用。基于现行的滤波算法都有一定的应用局限,本文提出了一种基于数据流的TIN滤波算法。该方法基于流的思想,首先对机载LiDAR数据进行点流的空间结点化,之后在构建Delaunay三角网的同时,进行插入点判断。通过试验区数据的滤波验证,此算法能够较好地滤除地物点,保持地形;提高了滤波效果的同时,在算法效率上还占有一定优势。     

基于数据流的TIN迭代滤波算法

通过机载LiDAR数据滤波获取地面信息是机载LiDAR数据的一项重要且基本的应用。基于现行的滤波算法都有一定的应用局限,本文提出了一种基于数据流的TIN滤波算法。该方法基于流的思想,首先对机载LiDAR数据进行点流的空间结点化,之后在构建Delaunay三角网的同时,进行插入点判断。通过试验区数据的滤波验证,此算法能够较好地滤除地物点,保持地形;提高了滤波效果的同时,在算法效率上还占有一定优势。     

一种新的机载LIDAR数据条带平差模型

机载LIDAR数据在DEM提取以及三维建模等方面正逐步得到广泛的应用,但是,残余系统误差的存在影响了LIDAR数据的应用,需要进行条带平差以消除残余系统误差。由于目前用户通常得不到原始的观测值,因此,本文在对机载LIDAR系统误差分析的基础上,针对系统检校后的LIDAR数据中存在的GPS定位和INS测姿误差,提出了一种条带平差的数学模型,该模型从LIDAR严格传感器模型出发,并且不需要系统原始的观测值。试验结果表明,该模型能够有效地提高LIDAR数据的精度。     

基于TerraSolid与Inpho的LiDAR数据处理方法分析与研究

介绍了机载激光扫描(Airborne Laser Scanning)/激光雷达(LiDAR)遥感信息获取系统的基本原理及系统组成,主要对基于TerraSolid和Inpho的两种LiDAR数据处理原理、滤波实现方法以及作业流程进行了分析,并通过不同地形特征的实验数据对其精度、效率、可操作性等方面进行了研究。     

高分三号卫星在极地冰区导航中的应用评价

针对面向极地冰区船舶航行导航的需求,利用我国第一颗民用合成孔径雷达卫星高分三号(GF-3)数据,通过定量化统计分析,开展了GF-3号标准条带模式数据在极地冰区应用潜力的评估。结果表明,GF-3号标准条带数据能较好地反映冰面的纹理,HH极化在图像层次和信息量上要优于HV极化;在图像的层次表达方面,GF-3/HV极化数据优于Radarsat-2/HV数据,GF-3/HH极化数据不如Radarsat-2/HH极化数据;GF-3数据能较好地反映南极冰区的纹理和类型特征,对于冰山、冰裂隙等冰上障碍具有较好的识别能力,对极地冰区导航和冰上卸货具备较好的应用效果。     

茂密植被区域LiDAR点云数据滤波方法研究

点云数据的滤波和分类是激光雷达数据应用处理重要环节,是当前研究的热点问题。本文针对茂密植被区域点云数据的特点,提出了以移动窗口和坡度算法为基础的改进的点云数据滤波算法。试验结果表明,改进的滤波算法对地形变化复杂、植被郁闭度较高覆盖、地面激光脚点比少的点云数据有良好的效果。     

Automated residential building detection from airborne LiDAR data with deep neural networks

Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.     

基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI

波形激光雷达（Light Detection And Ranging, LiDAR）已经大量用于森林叶面积指数（Leaf Area Index, LAI）估算,但是波形LiDAR数据估算森林LAI易受地形影响。地形坡度引起的波形展宽使得地面回波和植被冠层回波信息混合在一起,难以得到准确的地面回波和冠层回波,进而影响到LAI估算精度。为了估算不同地形坡度条件下的LAI,本文采用一种坡度自适应的方法处理机载LVIS和星载GLAS波形数据。通过坡度自适应的方法得到地面波峰位置,基于高度阈值来区分地面回波和冠层回波,进而得到能量比值用于LAI估算。基于LVIS和GLAS数据,估算了不同森林站点的LAI,并利用实测LAI数据进行检验。结果表明：利用波形LiDAR数据可以估算森林LAI,坡度自适应方法可以改善地形的影响,提高LAI估算精度。对于机载LVIS,估算新英格兰森林LAI精度为R²=0.77和RMSE=0.21;对于星载GLAS,估算塞罕坝森林LAI精度为R²=0.81和RMSE=0.28。无论机载还是星载数据,该方法都有着较高的精度,对于复杂地形估算LAI具有一定潜力。     

机载脉冲激光雷达剖面测量技术的进展及应用

机载脉冲激光雷达(LiDAR)剖面测量技术是一种先进的主动遥感测量技术,可以快速、大面积地直接获取地表地物、森林和水下地貌等的三维信息,具有机动性强、高效和实时等优点。该技术可用于海岸线海水深度测量,海岸生态状况监测,森林资源调查及地震等突发事件响应,具有巨大的应用潜力和广阔的发展前景。首先全面介绍了国外机载脉冲激光雷达剖面测量技术的发展历史,评述了各个发展阶段,并介绍了国内该技术的发展状况。然后分析了机载脉冲激光雷达剖面测量技术的主要应用领域。最后对机载脉冲激光雷达剖面测量技术的未来发展趋势作了展望。     

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。     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

Study on Power Tower Extraction and Fast Positioning from Airborne LiDAR Data

Airborne LiDAR has become an important technique for transmission line digitalization,reconstruction and safety inspection.Moreover,accurately and efficiently extracting the position of each tower from massive point clouds is basic and important task for the applications in power industry.In this study,a method was proposed to efficiently extract the point clouds and fast determine the position of power towers using airborne LiDAR data.Firstly,the point clouds of power towers were automatically separated from raw data based on the spatial distribution characteristics of airborne LiDAR data.Secondly,each power tower was efficiently detected using a region\|growing algorithm.Finally,a least square linear fitting method was used to determine the accurate position of each power tower.The new proposed method was applied to several LiDAR data sets in areas with high voltage transmission lines.Results indicated that the integrity of the power towers’ points is up to 91.1%,and the accuracy of center positions is high enough with the medium error of 13.5 cm.Additionally,our study also concluded that the proposed method is robust and applicable even the point density is relatively low.