Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data
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College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, China
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College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
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College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
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Department of Math & Computing Science, Saint Marys University, Halifax, NS B3P 2M6, Canada
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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mohammad Awrangjeb
Remote Sens. 2021, 13(3), 338; https://doi.org/10.3390/rs13030338
Received: 5 December 2020
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Revised: 9 January 2021
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Accepted: 18 January 2021
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Published: 20 January 2021
(This article belongs to the Section Remote Sensing Communications)
Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incompleteness and complex tree structures. Furthermore, these methods often require lots of preprocessing such as ground filtering and noise removal. The fast and easy-to-use top-based methods that are widely applied in processing ALS point clouds are not applicable in localizing trees in TLS point clouds due to the data incompleteness and complex canopy structures. The objective of this study is to make the top-based methods applicable to TLS forest point clouds. To this end, a novel point cloud transformation is presented, which enhances the visual salience of tree instances and makes the top-based methods adapting to TLS forest scenes. The input for the proposed method is the raw point clouds and no other pre-processing steps are needed. The new method is tested on an international benchmark and the experimental results demonstrate its necessity and effectiveness. Finally, the proposed method has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.
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MDPI and ACS Style
Xia, S.; Chen, D.; Peethambaran, J.; Wang, P.; Xu, S. Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data. Remote Sens. 2021, 13, 338. https://doi.org/10.3390/rs13030338
AMA Style
Xia S, Chen D, Peethambaran J, Wang P, Xu S. Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data. Remote Sensing. 2021; 13(3):338. https://doi.org/10.3390/rs13030338
Chicago/Turabian StyleXia, Shaobo, Dong Chen, Jiju Peethambaran, Pu Wang, and Sheng Xu. 2021. "Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data" Remote Sensing 13, no. 3: 338. https://doi.org/10.3390/rs13030338
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