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Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier
As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification me...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648668/ https://www.ncbi.nlm.nih.gov/pubmed/37960542 http://dx.doi.org/10.3390/s23218841 |
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author | Zhao, Danjing Ji, Linna Yang, Fengbao |
author_facet | Zhao, Danjing Ji, Linna Yang, Fengbao |
author_sort | Zhao, Danjing |
collection | PubMed |
description | As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification methods usually use a single machine learning classifier that experiences uncertainty in making decisions for fuzzy samples in confusing areas. This limits the improvement of classification accuracy. To take full advantage of different classifiers and reduce uncertainty, we propose a classification method based on possibility theory and multi-classifier fusion. Firstly, the feature importance measure was performed by the XGBoost algorithm to construct a feature space, and two commonly used support vector machines (SVMs) were the chosen base classifiers. Then, classification results from the two base classifiers were quantitatively evaluated to define the confusing areas in classification. Finally, the confidence degree of each classifier for different categories was calculated by the confusion matrix and normalized to obtain the weights. Then, we synthesize different classifiers based on possibility theory to achieve more accurate classification in the confusion areas. DALES datasets were utilized to assess the proposed method. The results reveal that the proposed method can significantly improve classification accuracy in confusing areas. |
format | Online Article Text |
id | pubmed-10648668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106486682023-10-31 Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier Zhao, Danjing Ji, Linna Yang, Fengbao Sensors (Basel) Article As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification methods usually use a single machine learning classifier that experiences uncertainty in making decisions for fuzzy samples in confusing areas. This limits the improvement of classification accuracy. To take full advantage of different classifiers and reduce uncertainty, we propose a classification method based on possibility theory and multi-classifier fusion. Firstly, the feature importance measure was performed by the XGBoost algorithm to construct a feature space, and two commonly used support vector machines (SVMs) were the chosen base classifiers. Then, classification results from the two base classifiers were quantitatively evaluated to define the confusing areas in classification. Finally, the confidence degree of each classifier for different categories was calculated by the confusion matrix and normalized to obtain the weights. Then, we synthesize different classifiers based on possibility theory to achieve more accurate classification in the confusion areas. DALES datasets were utilized to assess the proposed method. The results reveal that the proposed method can significantly improve classification accuracy in confusing areas. MDPI 2023-10-31 /pmc/articles/PMC10648668/ /pubmed/37960542 http://dx.doi.org/10.3390/s23218841 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Danjing Ji, Linna Yang, Fengbao Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title | Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title_full | Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title_fullStr | Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title_full_unstemmed | Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title_short | Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier |
title_sort | land cover classification based on airborne lidar point cloud with possibility method and multi-classifier |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648668/ https://www.ncbi.nlm.nih.gov/pubmed/37960542 http://dx.doi.org/10.3390/s23218841 |
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