Curvature Weighted Decimation: A Novel, Curvature-Based Approach to Improved Lidar Point Decimation of Terrain Surfaces

IF 2.3 Q2 REMOTE SENSING Applied Geomatics Pub Date : 2023-03-19 DOI:10.3390/geomatics3010015
P. Schrum, Carter Jameson, Laura G. Tateosian, G. Blank, K. Wegmann, S. A. Nelson
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引用次数: 1

Abstract

Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including large quantities of redundant points. Because of these large memory requirements, practitioners often use decimation to reduce the number of points used to create models. This paper introduces a novel approach to improve decimation, thereby reducing the total count of ground points in a Lidar dataset while retaining more accuracy than Random Decimation. This reduction improves efficiency of downstream processes while maintaining output quality nearer to the undecimated dataset. Points are selected for retention based on their discrete curvature values computed from the mesh geometry of the TIN model of the points. Points with higher curvature values are preferred for retention in the resulting point cloud. We call this technique Curvature Weighted Decimation (CWD). We implement CWD in a new free, open-source software tool, CogoDN, which is also introduced in this paper. We evaluate the effectiveness of CWD against Random Decimation by comparing the resulting introduced error values for the two kinds of decimation over multiple decimation percentages, multiple statistical types, and multiple terrain types. The results show that CWD reduces introduced error values over Random Decimation when 15 to 50% of the points are retained.
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曲率加权抽取:一种基于曲率的改进地形表面激光雷达点抽取的新方法
QL1/QL2激光雷达地形数据可用性的提高导致了大型数据集,通常包括大量冗余点。由于这些巨大的内存需求,从业者经常使用抽取来减少用于创建模型的点的数量。本文介绍了一种改进抽取的新方法,从而减少了激光雷达数据集中地面点的总数,同时保持了比随机抽取更高的精度。这种减少提高了下游过程的效率,同时保持输出质量更接近未消差数据集。根据点的TIN模型的网格几何计算出的离散曲率值来选择点进行保留。具有较高曲率值的点优先保留在生成的点云中。我们称这种技术为曲率加权抽取(CWD)。我们在一种新的免费开源软件工具CogoDN中实现了CWD,本文也对其进行了介绍。我们通过比较两种抽取在多个抽取百分比、多个统计类型和多个地形类型上的引入误差值,来评估CWD对随机抽取的有效性。结果表明,CWD在保留15% ~ 50%的点时,比随机抽取减少了引入的误差值。
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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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