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首页> 外文会议>International archives of the photogrammetry, remote sensing and spatial information sciences proceedings >CHANGE DETECTION FOR TOPOGRAPHIC MAPPING USING THREE-DIMENSIONAL DATA STRUCTURES
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CHANGE DETECTION FOR TOPOGRAPHIC MAPPING USING THREE-DIMENSIONAL DATA STRUCTURES

机译：使用三维数据结构改变地形映射的地形映射

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Identifying significant changes to our urban areas is a prerequisite for accurate topographic mapping. This paper presents an approach based on octree data structures to identify change between two sets of point cloud data. The aim of the study was to establish if a method based on the comparison of point clouds could be used to detect simply where topographic change had occurred. As many of the changes that could occur are likely to result in a change of real world geometry (for example the construction or demolition of a building) the use of geometric data -rather than imagery alone as in other studies -is justified. Additionally, it means input data can be supplied directly from airborne lidar systems, or from aerial imagery using digital photogrammetric workstations which still remain the most commonly used apparatus for national mapping activities.Octrees are data structures that allow the partitioning of three-dimensional data into increasingly smaller units of space, using predefined criteria to control the level of subdivision (in this case a limit on the number of points in a node; and/or the total level of subdivision). Octrees have previously been used in applications where efficient searching and inspection of large volumes of three-dimensional data is required, such as in the rendering of computer graphics. By defining these structures, large data volumes of non-connected data (such as point clouds) can be efficiently managed and quickly compared with similar datasets collected at different epochs. In the study presented here, one approach compares entire octrees for differences between their structures, while a second approach compares individual data points to data contained in a reference octree. Two UK test areas form the basis of the study. Area 1 is the site of Heathrow Airport's new Terminal Five which has seen significant development over the last five years. Area 2 is an urban/peri-urban area of Bournemouth consisting of both commercial and residential properties. In both cases, multi epoch data was provided by Ordnance Survey allowing point cloud data to be generated from imagery collected by an Intergraph DMC digital airborne sensor. High resolution photogrammetric processing was undertaken using BAE's Socet Set and point cloud pre-processing using Terrasolid's TerraScan suite. While it was possible to recognize significant and pre-identified areas of change using the methodology, a large number of false identifications were also observed, making it difficult to interpret the results without prior knowledge. The lack of success can partially be attributed to the quality of the input data. Slight variations in the point clouds, perhaps arising from poor image correlation during surface extraction, led to subtle variations in the structure of the octree and thus in the changes identified.
机译：确定我们城市地区的重大变化是准确地形映射的先决条件。本文介绍了一种基于OctREE数据结构的方法，以识别两组点云数据之间的变化。该研究的目的是建立一个基于点云比较的方法，可用于检测发生地形改变的情况。由于可能发生的许多变化可能导致现实世界几何形状的变化（例如建筑物或建筑物的拆除），使用几何数据 - 特单独的几何数据 - 在其他研究中，是合理的。此外，这意味着输入数据可以直接从机载激光雷达系统中使用数字摄影工作站仍保持国家制图activities.Octrees是数据结构，使三维数据的分割成最常用的设备来提供，或者从航空影像使用预定义标准越来越较小的空间单元，以控制细分级别（在这种情况下，节点中的点数限制;和/或细分的总级别）。先前已在需要高效搜索和检查大量的三维数据的应用中使用的应用中使用，例如计算机图形的渲染。通过定义这些结构，与在不同时期收集的类似数据集相比，可以有效地管理和快速地管理大数据量（例如点云）。在此处呈现的研究中，一种方法将整个八十件人与其结构之间的差异进行比较，而第二种方法将各个数据点与参考Octree中包含的数据进行比较。两个英国测试领域形成了该研究的基础。 1区是希思罗机场新码头的网站，在过去五年中有显着的发展。地区2是伯恩茅斯的城市/围城区，包括商业和住宅物业。在这两种情况下，使用由Intergraph DMC数字空气传播传感器收集的图像产生点云数据来提供多挪数据。使用Terasolid的Terrascan Suite使用Bae Socet Set和Point Cloud预处理进行了高分辨率摄影测量处理。虽然可以使用方法识别重要和预先确定的变化区域，但也观察到大量的错误识别，使得难以在未经事先知识的情况下解释结果。缺乏成功可以部分归因于输入数据的质量。点云的轻微变化，从表面提取期间的图像相关性差产生，导致八面体结构的微妙变化，因此在识别的变化中。

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