AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS
This article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/279/2023/isprs-annals-X-4-W1-2022-279-2023.pdf |
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| author | E. Hasanpour Zaryabi M. Saadatseresht E. Ghanbari Parmehr |
| author_facet | E. Hasanpour Zaryabi M. Saadatseresht E. Ghanbari Parmehr |
| author_sort | E. Hasanpour Zaryabi |
| collection | DOAJ |
| description | This article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D point coordinates into a semantic representation. The proposed framework has three main parts, including the development of a supervoxel data structure, point cloud segmentation based on local graphs, and using three methods for object-based classification. The results of the point cloud segmentation with an average segmentation error of 0.15 show that the supervoxel structure with an optimal parameter for the number of neighbors can reduce the computational cost and the segmentation error. Moreover, weighted local graphs that connect neighboring supervoxels and examine their similarities play a significant role in improving and optimizing the segmentation process. Finally, three classification methods including Random Forest, Gradient Boosted Trees, and Bagging Decision Trees were evaluated. As a result, the extracted segments were classified with an average precision of higher than 83%. |
| first_indexed | 2024-04-10T22:56:14Z |
| format | Article |
| id | doaj.art-c9f489b79e3e42d6ab60f276fe383690 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:56:14Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-c9f489b79e3e42d6ab60f276fe3836902023-01-14T11:13:08ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202227928610.5194/isprs-annals-X-4-W1-2022-279-2023AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREASE. Hasanpour Zaryabi0M. Saadatseresht1E. Ghanbari Parmehr2School of Surveying and Geospatial Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, University of Tehran, Tehran, IranBabol Noshirvani University of Technology, Dept. of Geomatics, Faculty of Civil Engineering, Babol, IranThis article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D point coordinates into a semantic representation. The proposed framework has three main parts, including the development of a supervoxel data structure, point cloud segmentation based on local graphs, and using three methods for object-based classification. The results of the point cloud segmentation with an average segmentation error of 0.15 show that the supervoxel structure with an optimal parameter for the number of neighbors can reduce the computational cost and the segmentation error. Moreover, weighted local graphs that connect neighboring supervoxels and examine their similarities play a significant role in improving and optimizing the segmentation process. Finally, three classification methods including Random Forest, Gradient Boosted Trees, and Bagging Decision Trees were evaluated. As a result, the extracted segments were classified with an average precision of higher than 83%.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/279/2023/isprs-annals-X-4-W1-2022-279-2023.pdf |
| spellingShingle | E. Hasanpour Zaryabi M. Saadatseresht E. Ghanbari Parmehr AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS |
| title_full | AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS |
| title_fullStr | AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS |
| title_full_unstemmed | AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS |
| title_short | AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS |
| title_sort | object based classification framework for als point cloud in urban areas |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/279/2023/isprs-annals-X-4-W1-2022-279-2023.pdf |
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