省级似大地水准面的局部再精化

本文利用少量新的高程异常控制点作为公共点,结合高精度的原省级似大地水准面模型,计算出公共点的高程异常残差,采用多面函数法对其进行数学拟合,获得待求点的高程异常改正数,可大幅提高省级似大地水准面模型的局部精度,是一种简单有效和切实可行的省级似大地水准面局部再精化方案,并给出了实例证明.     

基于组合罗德里格矩阵的异常特征点探测

特征点是点云配准的基础,针对异常特征点对点云配准精度的影响,本文给出了一种异常特征点探测方法,即根据罗德里格矩阵的性质,对特征点进行向量组合,对比分析不同向量组合的点云配准精度,确定配准误差超限的向量组合,此向量组合中含有而其他向量组合中不合有的特征点,便是异常特征点.通过实例验证了该方法的可行性.     

测量学实践教学的改革与实践

测量学是一门技术性和实践性很强的专业基础课,实践教学是整个教学过程中的重要环节。近几年来,测绘工程系以土木工程等6个本科专业为试点,从更新实践教学内容和教学方法、加强实践教学基础条件建设、创新实践教学考核评价方法等方面进行了系统的研究和大胆的改革,建立与完善了一套完整的测量学实践教学体系,在培养学生的合作精神和动手能力方面收到了良好效果。     

机载LiDAR点云数据精度评定方法的研究

基于机载LiDAR系统组成及工作原理,对激光点云数据精度评定方法进行研究,主要包括内插特征点对比分析法、统计分析法、斜面几何分析法、误差传播定律分析法。各种方法都有各自的优缺点,在进行具体精度评定时,几种方法要结合使用。     

基于地理信息云服务的现代企业管理模式

如今,现代企业对地理空间信息服务的需求日益增多,传统的信息管理模式已越来越不能满足企业发展的需求,企业管理模式急需全面革新与升级。本文从超图云服务在企业管理中的应用出发,结合GIS、云计算、网格化管理等先进的技术,描述了企业服务平台的7大可在线调用的功能模块,阐述了地理信息云服务在企业生产运营的整个作业链中给企业带来的价值。     

全站仪在日照测量中的应用

从日照测量的目的和任务出发,介绍全站仪在日照测量中的具体应用及注意事项,通过实例分析日照测量外业数据采集的原则和内业成图的要求,实践证明利用全站仪进行日照测量工作,具有低成本、高效率、易操作的优点.     

三维激光扫描技术在数字矿山领域的应用

随着数字矿山概念的提出,矿山管理对空间三维信息的需求也显得更加迫切,三维可视化的管理模式已经成为数字矿山的主要内容之一,而目前常规的测量方式所获取的数据很难满足三维数字矿山的需要。三维激光扫描技术具有高分辨率、高采样率以及非接触式测量优势,非常适用于获取矿山的复杂表面和高危区域的空间三维信息。本文结合三维激光扫描的技术优势探讨该技术在数字矿山中的应用和方法,总结了三维激光扫描技术在数字矿山领域的应用方向。     

组合法精化胶州市大地水准面

本文利用全球重力位模型、胶州市地面重力观测数据、胶州市GPS水准数据和数字地面模型(DTM),采用组合法应用移去-恢复技术计算剩余大地水准面,并与地球位模型计算的高程异常进行拟合,得到该地区重力似大地水准面,再和布测、计算得到的GPS/水准所构成的几何大地水准面拟合,利用多项式拟合完成系统改正,获得最终的大地水准面结果及相关的精度信息。     

PTrees: A point-based approach to forest tree extraction from lidar data

This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.     

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications.