基于ArcGIS的降水量空间插值方法研究

目前对降水量空间插值方法的研究很多,针对反距离权重插值、样条函数插值、普通克里金插值在降水量空间插值中的缺陷,提出引入月平均总云量影响因子的协同克里金插值方法。并以内蒙古降水量数据为例进行实验,实验结果表明,引入月平均总云量影响因子的协同克里金插值在精度和拟合度方面得到改善。     

3维激光扫描技术在石窟保护中的应用研究

高精度3维激光扫描技术的出现,为建立一套覆盖完整、细节清晰、视觉效果突出、数据真实准确的数字石窟提供了可能。云冈石窟3维数字化工作就是将3维激光扫描技术应用到石窟的制作,利用激光测距原理,以最精细、最合理的技术手段记录石窟的密集点的3维坐标、反射率和纹理等信息,完整保留全面、准确、细致的3维数据,进而复建出石窟的3维模型及线、面、体等各种数据,将云冈石窟清晰地展现出来。     

一种气象卫星云图的坐标转换与配准方法研究

设计了气象卫星云图坐标转换与范围截取的算法,并基于ArcEngine组件实现了卫星云图的自动配准。实验证明,本文提出的方法可以快速有效地实现卫星云图的位置校正以及与基础地理数据的准确叠加,减少分析过程中的误差,提高气象卫星云图的使用效率,对天气监测及分析预报等工作提供了及时、有效、科学的辅助决策支持。     

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.     

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。     

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。     

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.