Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning
•Combined methods for denoising and modelling of stem point clouds were validated.•Algorithms were tested on point clouds of varying sizes, without loss in performance.•Different species were scanned in order to test performances on different tree architectures and point cloud conditions. The presen...
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| Published in: | Computers and electronics in agriculture Vol. 143; pp. 165 - 176 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-12-2017
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| Abstract | •Combined methods for denoising and modelling of stem point clouds were validated.•Algorithms were tested on point clouds of varying sizes, without loss in performance.•Different species were scanned in order to test performances on different tree architectures and point cloud conditions.
The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees – thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance – achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality. |
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| AbstractList | The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees - thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance - achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality. The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees - thus vaildating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance - achieving 2.15cm of RMSE and 1.09cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality. •Combined methods for denoising and modelling of stem point clouds were validated.•Algorithms were tested on point clouds of varying sizes, without loss in performance.•Different species were scanned in order to test performances on different tree architectures and point cloud conditions. The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees – thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance – achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality. |
| Author | Almeida, Gustavo de Conto, Tiago Rodriguez, Luiz Carlos Estraviz Olofsson, Kenneth Görgens, Eric Bastos |
| Author_xml | – sequence: 1 givenname: Tiago surname: de Conto fullname: de Conto, Tiago email: tiagodc@usp.br organization: Swedish University of Agricultural Sciences (SLU), Sundsvägen 6, 230 53 Alnarp, Sweden – sequence: 2 givenname: Kenneth orcidid: 0000-0002-2836-2316 surname: Olofsson fullname: Olofsson, Kenneth organization: Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, 907 36 Umeå, Sweden – sequence: 3 givenname: Eric Bastos surname: Görgens fullname: Görgens, Eric Bastos organization: Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Campus JK, Alto da Jacuba, 39100-000 Diamantina, MG, Brazil – sequence: 4 givenname: Luiz Carlos Estraviz surname: Rodriguez fullname: Rodriguez, Luiz Carlos Estraviz organization: Escola Superior de Agricultura Luiz de Queiroz – Universidade de São Paulo (ESALQ-USP), Avenida Pádua Dias, 11, 13418-900 Piracicaba, SP, Brazil – sequence: 5 givenname: Gustavo surname: Almeida fullname: Almeida, Gustavo organization: Escola Superior de Agricultura Luiz de Queiroz – Universidade de São Paulo (ESALQ-USP), Avenida Pádua Dias, 11, 13418-900 Piracicaba, SP, Brazil |
| BackLink | https://res.slu.se/id/publ/91417$$DView record from Swedish Publication Index |
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| Keywords | RANSAC Robust estimation Cylinder/circle fit Tropical and boreal tree species LiDAR |
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| Snippet | •Combined methods for denoising and modelling of stem point clouds were validated.•Algorithms were tested on point clouds of varying sizes, without loss in... The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser... |
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| SubjectTerms | Algorithms Annan geovetenskap och miljövetenskap Cylinder/circle fit Cylinders Fjärranalysteknik Hough transformation LiDAR Modelling Noise reduction Other Earth and Related Environmental Sciences RANSAC Remote Sensing Robust estimation Scanners Segments Studies Three dimensional models Tropical and boreal tree species |
| Title | Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning |
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