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Research Article

R-ProjNet: an optimal rotated-projection neural network for wood segmentation from point clouds

ORCID Icon, , &
Pages 60-69 | Received 21 Aug 2022, Accepted 18 Nov 2022, Published online: 28 Dec 2022

References

  • Balenović, I., X. Liang, L. Jurjević, J. Hyyppä, A. Seletković, and A. Kukko. 2021. “Hand-Held Personal Laser Scanning–Current Status and Perspectives for Forest Inventory Application.” Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering 42 (1): 165–183. doi:10.5552/crojfe.2021.858.
  • Chen, Y., S. Wang, J. Li, L. Ma, R. Wu, Z. Luo, and C. Wang. 2019. “Rapid Urban Roadside Tree Inventory Using a Mobile Laser Scanning System.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (9): 3690–3700. doi:10.1109/JSTARS.2019.2929546.
  • Fan, W., W. Chenglu, and L. Jonathan. 2016. “Automated Extraction of Urban Trees from Mobile Lidar Point Clouds.” 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), International Society for Optics and Photonics, Xiamen, China, vol 9901, p. 99010. https://doi.org/10.1117/12.2234795.
  • Guan, H., Y. Yu, J. Li, and P. Liu. 2016. “Pole-Like Road Object Detection in Mobile Lidar Data via Supervoxel and Bag-Of-Contextual-Visual-Words Representation.” IEEE Geosci Remote S 13 (4): 520–524.
  • Hui, Z., S. Jin, Y. Xia, L. Wang, Y. Y. Ziggah, and P. Cheng. 2021. “Wood and Leaf Separation from Terrestrial Lidar Point Clouds Based on Mode Points Evolution.” Isprs Journal of Photogrammetry and Remote Sensing 178: 219–239. doi:10.1016/j.isprsjprs.2021.06.012.
  • Hu, C., Z. Pan, and T. Zhong. 2020. “Leaf and Wood Separation of Poplar Seedlings Combining Locally Convex Connected Patches and K-Means++ Clustering from Terrestrial Laser Scanning Data.” Journal of Applied Remote Sensing 14 (1): 1. doi:10.1117/1.JRS.14.018502.
  • Li, Q., P. Yuan, X. Liu, and H. Zhou. 2020. “Street Tree Segmentation from Mobile Laser Scanning Data.” International Journal of Remote Sensing 41 (18): 7145–7162. doi:10.1080/01431161.2020.1754495.
  • Moorthy, S. M. K., Y. Bao, K. Calders, S. A. Schnitzer, and H. Verbeeck. 2019. “Semi-Automatic Extraction of Liana Stems from Terrestrial Lidar Point Clouds of Tropical Rainforests.” Isprs Journal of Photogrammetry and Remote Sensing 154: 114–126. doi:10.1016/j.isprsjprs.2019.05.011.
  • Moorthy, S. M. K., K. Calders, M. B. Vicari, and H. Verbeeck. 2020. “Improved Supervised Learning-Based Approach for Leaf and Wood Classification from LiDar Point Clouds of Forests.” IEEE Transactions on Geoscience and Remote Sensing 58 (5): 3057–3070. doi:10.1109/tgrs.2019.2947198.
  • Raumonen, P., M. Kaasalainen, M. Åkerblom, S. Kaasalainen, H. Kaartinen, M. Vastaranta, M. Holopainen, M. Disney, and P. Lewis. 2013. “Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data.” Remote Sensing 5 (2): 491–520. doi:10.3390/rs5020491.
  • Sun, C., C. Huang, H. Zhang, B. Chen, F. An, L. Wang, and T. Yun. 2022. “Individual Tree Crown Segmentation and Crown Width Extraction from a Heightmap Derived from Aerial Laser Scanning Data Using a Deep Learning Framework.” Frontiers in plant science 13. doi:10.3389/fpls.2022.914974.
  • Vallet, B., M. Brédif, A. Serna, B. Marcotegui, and N. Paparoditis. 2015. “Terramobilita/Iqmulus Urban Point Cloud Analysis Benchmark.” Computers & Graphics 49: 126–133. doi:10.1016/j.cag.2015.03.004.
  • Vatandaşlar, C., and M. Zeybek. 2021. “Extraction of Forest Inventory Parameters Using Handheld Mobile Laser Scanning: A Case Study from Trabzon, Turkey.” Measurement 177 (109): 328. doi:10.1016/j.measurement.2021.109328.
  • Wang, D. 2020. “Unsupervised Semantic and Instance Segmentation of Forest Point Clouds.” Isprs Journal of Photogrammetry and Remote Sensing 165: 86–97. doi:10.1016/j.isprsjprs.2020.04.020.
  • Windrim, L., and M. Bryson. 2020. “Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning.” Remote Sensing 12 (9): 1469. doi:10.3390/rs12091469.
  • Xia, S., C. Wang, F. Pan, X. Xi, H. Zeng, and H. Liu. 2015. “Detecting Stems in Dense and Homogeneous Forest Using Single-Scan Tls.” Forests 6 (11): 3923–3945. doi:10.3390/f6113923.
  • Xi, Z., C. Hopkinson, and L. Chasmer. 2018. “Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks.” Remote Sensing 10 (8): 1215. doi:10.3390/rs10081215.
  • Xu, Y., C. Hu, and Y. Xie. 2022. “An Improved Space Colonization Algorithm with Dbscan Clustering for a Single Tree Skeleton Extraction.” International Journal of Remote Sensing 43 (10): 3692–3713. doi:10.1080/01431161.2022.2102950.
  • Xu, S., N. Ye, S. Xu, and F. Zhu. 2018. “A Supervoxel Approach to the Segmentation of Individual Trees from Lidar Point Clouds.” Remote Sensing Letters 9 (6): 515–523. doi:10.1080/2150704X.2018.1444286.
  • Yun, T., K. Jiang, G. Li, M. P. Eichhorn, J. Fan, F. Liu, B. Chen, F. An, and L. Cao. 2021. “Individual Tree Crown Segmentation from Airborne Lidar Data Using a Novel Gaussian Filter and Energy Function Minimization-Based Approach.” Remote Sensing of Environment 256 (112): 307. doi:10.1016/j.rse.2021.112307.
  • Zhang, W., P. Wan, T. Wang, S. Cai, Y. Chen, X. Jin, and G. Yan. 2019. “A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data.” Remote Sensing 11 (2): 211. doi:10.3390/rs11020211.

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