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3D Point Cloud Semantic Segmentation Through Functional Data Analysis
dc.contributor.author | Oviedo de la Fuente, Manuel | |
dc.contributor.author | Cabo, Carlos | |
dc.contributor.author | Roca-Pardiñas, Javier | |
dc.contributor.author | Loudermilk, E. Louise | |
dc.contributor.author | Ordóñez, Celestino | |
dc.date.accessioned | 2024-06-27T10:50:29Z | |
dc.date.available | 2024-06-27T10:50:29Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Oviedo de la Fuente, M., Cabo, C., Roca-Pardiñas, J., Loudermilk, E. L., & Ordóñez, C. (2023). 3D Point Cloud Semantic Segmentation Through Functional Data Analysis. Journal of Agricultural, Biological, and Environmental Statistics. https://doi.org/10.1007/S13253-023-00567-W | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/37481 | |
dc.description | Financiado para publicación en acceso aberto: CRUE-CSIC/Springer Nature. | es_ES |
dc.description.abstract | [Abstract]: Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction. | es_ES |
dc.description.sponsorship | This research/work was supported by MINECO, PID2020-113578RB-I00, MTM2017-82724-R and PID2020-116587GB-I00 grants, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. “CITIC, a Research Center accredited by the Galician University System, is funded by the ”Consellería de Cultura, Educación e Universidade of the Xunta de Galicia. It was also funded in part by the US Department of Defense’s Strategic Environmental Research and Development Program, project no. RC19-1119. We acknowledge Tall Timbers Research Station for their support, including Dr. Robertson and Pebble Hill Plantation. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or US Government determination or policy”. Carlos Cabo received funding from the UK Natural Environment Research Council (NE/T001194/1), and from the Spanish Government (Ministry of Universities) and the European Union (NextGenerationEU), within the project MU21-UP2021-030. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Universidad de Oviedo; MU-21-UP2021-030 | es_ES |
dc.description.sponsorship | United States. Department of Defense’s Strategic Environmental Research and Development Program; RC19-1119 | es_ES |
dc.description.sponsorship | United Kingdom. Natural Environment Research Council; NE/T001194/1 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/MÉTODOS ESTADÍSTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORÍA Y APLICACIONES | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116587GB-I00/ES/DINAMICA COMPLEJA E INFERENCIA NO PARAMETRICA | es_ES |
dc.relation.uri | https://doi.org/10.1007/s13253-023-00567-w | es_ES |
dc.rights | Attribution 4.0 International License (CC BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Functional data | es_ES |
dc.subject | Laser scanning | es_ES |
dc.subject | Multiclass classification | es_ES |
dc.subject | Multiscale analysis | es_ES |
dc.subject | Variable selection | es_ES |
dc.title | 3D Point Cloud Semantic Segmentation Through Functional Data Analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Journal of Agricultural, Biological, and Environmental Statistics | es_ES |
dc.identifier.doi | 10.1007/s13253-023-00567-w |
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