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首页> 外文学位 >Vertical object extraction from full-waveform lidar data using a three-dimensional wavelet-based approach.
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Vertical object extraction from full-waveform lidar data using a three-dimensional wavelet-based approach.

机译：使用基于三维小波的方法从全波形激光雷达数据中提取垂直目标。

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摘要

An active area of research over the past several years has been the application of airborne light detection and ranging (lidar) in airport obstruction surveying. The primary objective in an airport obstruction survey is to accurately geolocate vertical objects, such as trees, towers, buildings, poles, and antennas, on and around airfields and in runway approaches. Previous studies have shown that airborne lidar is a potentially viable alternative to more expensive, time-consuming, traditional field and photogrammetric surveys for some airports and approach types. However, several problems remain unsolved. Due to lack of reliable, automated methods for extracting and attributing airport obstructions from lidar data, as well as stringent controls needed to ensure flight safety, extensive manual labor by skilled human analysts is typically needed. This, in turn, significantly reduces intended cost and time savings.;In this work, a new approach to extraction of vertical objects (airport obstructions, in particular) from airborne lidar data is developed and tested. The approach is specifically designed to exploit additional data provided by the latest small-footprint, full-waveform lidar systems, which digitally record the entire return signal from each transmitted pulse at high sampling frequencies. The primary steps in the approach include: (1) applying an advanced deconvolution algorithm to lidar waveforms, followed by georeferencing to produce very dense, detailed point clouds in which vertical structures of objects are well characterized; (2) voxelizing the lidar point clouds to generate high-resolution 3D grids (volumes) of lidar intensity values; (3) computing a 3D wavelet decomposition; and (4) performing vertical object detection and recognition in the wavelet domain.;The approach is tested using lidar waveform data collected with a commercial system in two Madison, Wisconsin, project areas. The reference data consist of field-surveyed vertical objects, including towers, antennas, trees, poles, and buildings, as well as high-resolution aerial imagery. The results illustrate the advantages of full-waveform data, volume representations, and multiresolution wavelet analysis for airport obstruction surveying and related vertical object detection applications. An additional benefit of this work is the demonstration of a highly effective and efficient workflow for lidar airport obstruction surveys.
机译：过去几年中，一个活跃的研究领域是机载光检测和测距（激光雷达）在机场障碍物测量中的应用。机场障碍物调查的主要目的是在飞机场上和跑道周围以及跑道进场中准确定位垂直对象，例如树木，塔楼，建筑物，电线杆和天线。先前的研究表明，对于某些机场和进场类型，机载激光雷达可以替代更昂贵，耗时的传统野外测绘和摄影测量。但是，仍有几个问题尚未解决。由于缺乏可靠的，自动的方法来从激光雷达数据中提取和识别机场障碍物，以及缺乏确保飞行安全所需的严格控制措施，因此通常需要熟练的人工分析人员进行大量的体力劳动。从而大大减少了预期的成本和时间节省。在这项工作中，开发并测试了一种新的方法，该方法可以从机载激光雷达数据中提取垂直物体（尤其是机场障碍物）。该方法经过专门设计，可利用最新的小尺寸，全波形激光雷达系统提供的附加数据，该系统以高采样频率数字记录每个发射脉冲的全部返回信号。该方法的主要步骤包括：（1）对激光雷达波形应用先进的反卷积算法，然后进行地理配准以生成非常密集，详细的点云，其中可以很好地表征物体的垂直结构; （2）对激光雷达点云进行体素化以生成激光雷达强度​​值的高分辨率3D网格（体积）; （3）计算3D小波分解; （4）在小波域中执行垂直目标检测和识别。该方法是使用在威斯康星州麦迪逊市两个项目区域中使用商业系统收集的激光雷达波形数据进行测试的。参考数据包括现场观测的垂直对象，包括塔，天线，树木，电线杆和建筑物，以及高分辨率的航空影像。结果说明了全波形数据，体积表示和多分辨率小波分析在机场障碍物测量和相关垂直物体检测应用中的优势。这项工作的另一个好处是演示了激光雷达机场障碍物调查的高效工作流程。

著录项

  • 作者

    Parrish, Christopher E.;

  • 作者单位

    The University of Wisconsin - Madison.;

  • 授予单位 The University of Wisconsin - Madison.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 171 p.
  • 总页数 171
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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