基于异速标度理论的滤波器改进定量结构模型

IF 2.3 Q2 REMOTE SENSING
Jan Hackenberg, Jean-Daniel Bontemps
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引用次数: 0

摘要

定量结构模型(QSMs)是树木的拓扑有序圆柱体模型,涵盖了从茎的基部到所有尖端的完整分支结构。但是细分支在输入点云中显得太大。这导致了一个众所周知的问题,即高估了QSM圆柱体在细分支中的体积和半径。我们在这里提出了一个解决这个问题的方法,通过引入两个QSM滤波器来校正这些圆柱体的半径。滤波器本身是建立在异速缩放理论的理论基础之上的。为了验证,我们使用SimpleForest软件从开放点云数据集生成的QSMs。我们将65棵被砍伐树木的QSM体积与采伐的参考数据进行了比较。我们还发现了TreeQSM的QSM数据,TreeQSM是一个具有竞争力且被广泛接受的QSM建模工具,使用不同的过滤方法。我们的方法在三种不同的误差测量上表现得更准确。我们量化我们方法的误差RMSE为127 \(\mathtt {dm^{3}}\), \(\mathtt {r^{2}_{adj.}}\)为0.96,CCC为0.97。有了这些过滤器,估计树木总体积或部分体积的准确性确实大大提高了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving quantitative structure models with filters based on allometric scaling theory

Quantitative structure models (QSMs) are topological ordered cylinder models of trees which cover the complete branching structure from the stem’s base up to all tips. But the thin branches appear too large in the input point clouds. This leads to a well known problem, the overestimation of the QSM cylinders’ volumes and radii in thin branches. We present here a solution to this problem by introducing two QSM filters correcting the radii of such cylinders. The filters itself are build upon the theoretical fundamentals of allometric scaling theories. For validation we use QSMs produced from an open point cloud data set of tree clouds with the SimpleForest software. We compare the QSM volume against the harvested reference data for 65 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool utilizing a different filter method. Our method performed more accurate on three different error measures. We quantify the error of our method with a RMSE of 127 \(\mathtt {dm^{3}}\), a \(\mathtt {r^{2}_{adj.}}\) of 0.96 and a CCC of 0.97. With those filters the accuracy of estimating total or partial volume of trees does significantly increase.

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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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