Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof
Article
Tarsha Kurdi, Fayez, Gharineiat, Zahra, Campbell, Glenn, Awrangjeb, Mohammad and Dey, Emon Kumar. 2022. "Automatic Filtering of Lidar Building Point Cloud in Case of
Trees Associated to Building Roof." Remote Sensing. 14 (2), pp. 1-23. https://doi.org/10.3390/rs14020430
| Article Title | Automatic Filtering of Lidar Building Point Cloud in Case of |
|---|---|
| ERA Journal ID | 201448 |
| Article Category | Article |
| Authors | Tarsha Kurdi, Fayez (Author), Gharineiat, Zahra (Author), Campbell, Glenn (Author), Awrangjeb, Mohammad (Author) and Dey, Emon Kumar (Author) |
| Journal Title | Remote Sensing |
| Journal Citation | 14 (2), pp. 1-23 |
| Article Number | 430 |
| Number of Pages | 23 |
| Year | 2022 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2072-4292 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/rs14020430 |
| Web Address (URL) | https://www.mdpi.com/2072-4292/14/2/430 |
| Abstract | This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach. |
| Keywords | LiDAR; classification; modelling; filtering; segmentation |
| ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
| Byline Affiliations | School of Surveying and Built Environment |
| Griffith University | |
| Institution of Origin | University of Southern Queensland |
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