Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: May 2020
Next issue: Aug 2020
Avg review time: 69 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,637,378 unique visits
670,793 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 20 (2020)
 
     »   Issue 2 / 2020
 
     »   Issue 1 / 2020
 
 
 Volume 19 (2019)
 
     »   Issue 4 / 2019
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
  View all issues  


FEATURED ARTICLE

Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
Issue 2/2018

AbstractPlus


SAMPLE ARTICLES

Tuning Logic Simulator for Estimation of VLSI Timing Degradation under Aging, MILIC, M.
Issue 3/2019

AbstractPlus

Optimal Transceiver Design for SWIPT in Interference Alignment Network, CHEN, Y., LIU, C., FU, Y., SONG, Y., QIAN, M.
Issue 2/2020

AbstractPlus

A Novel Test Method for Real-time Magnetic Flux Measurement of Power Transformers, ZHANG, Y., DAI, D., ZHANG, J., LIU, X., CHEN, X.
Issue 4/2019

AbstractPlus

A New V2G Control Strategy for Load Factor Improvement Using Smoothing Technique, CHANHOM, P., NUILERS, S., HATTI, N.
Issue 3/2017

AbstractPlus

Improving Voltage Profile and Optimal Scheduling of Vehicle to Grid Energy based on a New Method, NAZARLOO, A., FEYZI, M. R., SABAHI, M., BANNAE SHARIFIAN, M. B.
Issue 1/2018

AbstractPlus

Fault Detection Variants of the CloudBus Protocol for IoT Distributed Embedded Systems, BARKALOV, A., TITARENKO, L., ANDRZEJEWSKI, G., KRZYWICKI, K., KOLOPIENCZYK, M.
Issue 2/2017

AbstractPlus




LATEST NEWS

2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

2019-Dec-16
Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

Read More »


    
 

  3/2019 - 2

Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud Data

NING, X. See more information about NING, X. on SCOPUS See more information about NING, X. on IEEExplore See more information about NING, X. on Web of Science, TIAN, G. See more information about  TIAN, G. on SCOPUS See more information about  TIAN, G. on SCOPUS See more information about TIAN, G. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,044 KB) | Citation | Downloads: 303 | Views: 590

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
Quick view
Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

[1] J. U. Duncombe, "Infrared navigation Part I: An assessment of Weinmann, M., Jutzi, B., Hinz, S., Mallet, C. "Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers". Isprs Journal of Photogrammetry and Remote Sensing. vol, 105, pp. 286-304, 2015.
[CrossRef] [Web of Science Times Cited 167] [SCOPUS Times Cited 235]


[2] Li, L., Li, D., Zhu, H., Li, Y. "A dual growing method for the automatic extraction of individual trees from mobile laser scanning data". Isprs Journal of Photogrammetry and Remote Sensing. vol.120, pp.37-52, 2016.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 28]


[3] G. Vosselman, "Point cloud segmentation for urban scene classification," ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W2(7), 257-262 (2013).
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 59]


[4] Jaakkola, A., Hyyppa, J., Kukko, A., Yu, X., Kaartinen, H., Lehtomaki, M., Lin, Y. "A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements". Isprs Journal of Photogrammetry and Remote Sensing. vol.65, no.6, pp.514-522, 2010.
[CrossRef] [Web of Science Times Cited 189] [SCOPUS Times Cited 207]


[5] A., Bienert, S., Scheller, E. Keane, F. Mohan, and C. Nugent. "Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds," in ISPRS Workshop on Laser Scanning 2007 and SilvilLaser 2007, 50-55 (2012)

[6] Raumonen, Kaasalainen, Mikko, Akerblom, Markku, Kaasalainen, Sanna, Kaartinen, Harri. "Fast automatic precision tree models from terrestrial laser scanner data". Remote Sensing. vol.5, no.2, pp. 491-520. 2013.
[CrossRef] [Web of Science Times Cited 254] [SCOPUS Times Cited 264]


[7] Wang, Y., Weinacker, H., Koch, B. "A lidar point cloud based procedure for vertical canopy structure analysis and 3d single tree modelling in forest". Sensors. vol.8, no.6, pp. 3938-3951, 2008.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 106]


[8] B. Sirmacekand, R. Lindenbergh,"Automatic classification of trees from laser scanning point clouds," ISPRS Annals of Photogrammetry Remote Sensing and Spatial Informa II-3/W5(4), 137-144 (2015).
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 14]


[9] R. C. Lindenbergh, D. Berthold, B. Sirmacek, et al., "Automated large scale parameter extraction of road-side trees sampled by a laser mobile mapping system," International Archives of the Photogrammetry Remote Sensing and S XL-3/W3, 589-594 (2015).
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[10] Jutras, P., Prasher, S.O., Mehuys, G.R. "Prediction of street tree morphological parameters using artificial neural networks". Computers and Electronics in Agriculture. vol.67, no.1, pp. 9-17, 2009.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 15]


[11] Puttonen, E., Jaakkola, A., Litkey, P., Hyyppa, J. "Tree classification with fused mobile laser scanning and hyperspectral data". Sensors vol.11, no.5, pp. 5158-5182, 2011.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 53]


[12] Li, J., Hu, B., Noland, T.L. "Classification of tree species based on structural features derived from high density lidar data". Agricultural and Forest Meteorology. vol.171-172, pp. 104-114, 2013.
[CrossRef]


[13] Wu, B., Yu, B., Yue, W., Shu, S., Tan, W., Hu, C., Huang, Y., Wu, J., Liu, H. "A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data". Remote Sensing. vol.5, no.2, pp. 584-611, 2013.
[CrossRef] [Web of Science Times Cited 106] [SCOPUS Times Cited 114]


[14] Xiang, B., Yao, J., Lu, X., Li, L., Xie, R. "Segmentation-based classification for 3d urban point clouds". In: IEEE International Conference on Information and Automation. pp. 172-177, 2017.
[CrossRef] [SCOPUS Times Cited 7]


[15] Barnea, S., Filin, S. "Segmentation of terrestrial laser scanning data using geometry and image information". International Journal of Photogrammetry and Remote Sensing. vol.6, no.1, pp. 33-48, 2013.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 44]


[16] Pu, S., Rutzinger, M., Vosselman, G., Elberink, S.O. "Recognizing basic structures from mobile laser scanning data for road inventory studies". Isprs Journal of Photogrammetry and Remote Sensing. vol.66, no.6, pp. S28-S39, 2011.
[CrossRef] [Web of Science Times Cited 183] [SCOPUS Times Cited 239]


[17] Yang, B., Dong, Z. "A shape-based segmentation method for mobile laser scanning point clouds". Isprs Journal of Photogrammetry and Remote Sensing. vol.81, no.7, pp. 19-30, 2013.
[CrossRef] [Web of Science Times Cited 105] [SCOPUS Times Cited 137]


[18] D. Munoz, J. A. Bagnell, N. Vandapel, et al., "Contextual classification with functional max-margin markov networks," in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 975-982 (2009).
[CrossRef]


[19] S. Xu, S. Xu, N. Ye, et al., "Automatic extraction of street trees non-photosynthetic components from mls data," International Journal of Applied Earth Observation and Geoinformation 69 (2018).
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 10]


[20] M. Lehtomaki, A. Jaakkola, J. Hyypp, et al., "Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data," Remote Sensing 2(3), 641-664 (2010).
[CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 172]


[21] N. H. Arachchige, "Automatic tree stem detection - a geometric feature based approach for mls point clouds," II-5/W2, 109-114 (2013).
[CrossRef] [SCOPUS Times Cited 9]


[22] X. Liang, J. Hyyppa, A. Kukko, et al., "The use of a mobile laser scanning system for map- ping large forest plots," IEEE Geoscience and Remote Sensing Letters 11(9), 1504-1508 (2014).
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 71]


[23] S. Xia, C. Wang, X. Pan, F.and Xi, et al., "Detecting stems in dense and homogeneous forest using single-scan tls," Forests 6(11), 3923-3945 (2015).
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 27]


[24] S. Xu, N. Ye, S. Xu, et al., "A supervoxel approach to the segmentation of individual trees from lidar point clouds," Remote Sensing Letters 9(6), 515-523 (2018).
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]


[25] Hyyppa, J., Kelle, O., Lehikoinen, M., Inkinen, M. "A segmentation-based method to retrieve stem volume estimates from 3-d tree height models produced by laser scanners". vol.39, no.5, pp. 969-975, 2001.
[CrossRef] [Web of Science Times Cited 435] [SCOPUS Times Cited 467]


[26] Lalonde, J.F., Vandapel, N., Huber, D.F., Hebert, M. "Natural terrain classification using three dimensional ladar data for ground robot mobility". Journal of Field Robotics. vol. 23, no.10, pp. 839-861, 2006.
[CrossRef] [Web of Science Times Cited 232] [SCOPUS Times Cited 301]


[27] Rutzinger, M., Pratihast, A.K., Elberink, S.J.O., Vosselman, G. "Tree modelling from mobile laser scanning datasets". Photogrammetric Record. Vol.26, no.135, pp.361-372. 2011.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 62]


[28] F. Monnier, B. Vallet, and B. Soheilian, "Trees detection from laser point clouds acquired in dense urban areas by a mobile mapping system," in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXII ISPRS Congress, I-3(I-3) (2012).
[CrossRef] [SCOPUS Times Cited 42]


[29] W. Yao and H. Fan, "Automated detection of 3d individual trees along urban road corridors by mobile laser scanning systems," in International Symposium on Mobile Mapping Technology, (2013).

[30] Zhong, R., Wei, J., Su, W., Chen, Y.F. "A method for extracting trees from vehicle-borne laser scanning data". Mathematical and Computer Modelling. vol.58, no.3-4, pp. 727-736, 2013.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 21]


[31] P. Raumonen, M. Kaasalainen, M. Kerblom,et al., "Fast automatic precision tree models from terrestrial laser scanner data," Remote Sensing 5(2), 491-520 (2013).
[CrossRef] [Web of Science Times Cited 254] [SCOPUS Times Cited 264]


[32] W. Fan, W. Chenglu, L. Jonathan, et al., "Automated extraction of urban trees from mobile lidar point clouds," in Isprs International Conference on Computer Vision in Remote Sensing, 99010P (2016).
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]


[33] Zhong L., Cheng, L., Xu, H., Wu, Y., Chen, Y., Li, M. "Segmentation of individual trees from tls and mls data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(2), 774-787 (2017).
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 26]


[34] J. Trochta, M. Krucek, T. Vrska, et al., "3d forest: An application for descriptions of three- dimensional forest structures using terrestrial lidar," Plos One 12(5), e0176871 (2017).
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 45]




References Weight

Web of Science® Citations for all references: 2,575 TCR
SCOPUS® Citations for all references: 3,054 TCR

Web of Science® Average Citations per reference: 74 ACR
SCOPUS® Average Citations per reference: 87 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-08-11 14:59 in 218 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2020
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.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

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.




Website loading speed and performance optimization powered by: