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Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud DataNING, X.   , TIAN, G. , WANG, Y. |
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Author keywords
computer graphics, computer aided analysis, feature extraction, object segmentation, pattern recognition
References keywords
laser(19), sensing(18), remote(18), scanning(13), mobile(13), data(13), trees(11), tree(11), point(11), photogrammetry(10)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03002
Web of Science Accession Number: 000486574100002
SCOPUS ID: 85072171970
Abstract
Urban trees are essential elements in outdoor scenes recorded via terrestrial laser scanning. Although considerable interest has been centered on tree detection and reconstruction in recent years, trees cannot be easily extracted from dense and unorganized data because of the complexity and diversity of trees. In this paper, we present a top-down approach for detecting trees from point cloud data acquired for dense urban areas. Appropriate feature subsets are chosen, and then the candidate tree clusters are selected via a binary classification. After distinguishing the 3D points belonging to tree-like objects, individual trees are extracted by spectral clustering. Furthermore, a weighted constraint rule is proposed to refine the individual tree clusters. The methodology is tested on five real-world datasets that include different varieties of trees. The results reveal that most of the individual trees can be correctly detected and extracted. The results are quantitatively evaluated and reveal a global F1 value of approximately 97 percent and a precision of approximately 98 percent. Comparative analysis on the datasets is also provided to prove the effectiveness of our proposed method. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
