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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8841 |
| _version_ | 1797631271404306432 |
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| author | Danjing Zhao Linna Ji Fengbao Yang |
| author_facet | Danjing Zhao Linna Ji Fengbao Yang |
| author_sort | Danjing Zhao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T11:21:28Z |
| format | Article |
| id | doaj.art-819cf8dec5a74c56b112ad67c74c4e01 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:28Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-819cf8dec5a74c56b112ad67c74c4e012023-11-10T15:12:20ZengMDPI AGSensors1424-82202023-10-012321884110.3390/s23218841Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-ClassifierDanjing Zhao0Linna Ji1Fengbao Yang2School of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaAs 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.https://www.mdpi.com/1424-8220/23/21/8841possibility theoryclassifier fusionland cover classificationpoint cloudSVMALS |
| spellingShingle | Danjing Zhao Linna Ji Fengbao Yang Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier Sensors possibility theory classifier fusion land cover classification point cloud SVM ALS |
| 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 | possibility theory classifier fusion land cover classification point cloud SVM ALS |
| url | https://www.mdpi.com/1424-8220/23/21/8841 |
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