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Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds
This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855963/ https://www.ncbi.nlm.nih.gov/pubmed/29401656 http://dx.doi.org/10.3390/s18020448 |
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author | Tran, Thi Huong Giang Ressl, Camillo Pfeifer, Norbert |
author_facet | Tran, Thi Huong Giang Ressl, Camillo Pfeifer, Norbert |
author_sort | Tran, Thi Huong Giang |
collection | PubMed |
description | This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground. |
format | Online Article Text |
id | pubmed-5855963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58559632018-03-20 Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds Tran, Thi Huong Giang Ressl, Camillo Pfeifer, Norbert Sensors (Basel) Article This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground. MDPI 2018-02-03 /pmc/articles/PMC5855963/ /pubmed/29401656 http://dx.doi.org/10.3390/s18020448 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tran, Thi Huong Giang Ressl, Camillo Pfeifer, Norbert Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title | Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title_full | Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title_fullStr | Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title_full_unstemmed | Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title_short | Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds |
title_sort | integrated change detection and classification in urban areas based on airborne laser scanning point clouds |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855963/ https://www.ncbi.nlm.nih.gov/pubmed/29401656 http://dx.doi.org/10.3390/s18020448 |
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