Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data
Article
Tarsha Kurdi, Fayez, Amakhchan, Wijdan, Gharineiat, Zahra, Boulaassal, Hakim and Kharki, Omar El. 2023. "Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data." Sensors. 23 (17). https://doi.org/10.3390/s23177360
| Article Title | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
|---|---|
| ERA Journal ID | 34304 |
| Article Category | Article |
| Authors | Tarsha Kurdi, Fayez, Amakhchan, Wijdan, Gharineiat, Zahra, Boulaassal, Hakim and Kharki, Omar El |
| Journal Title | Sensors |
| Journal Citation | 23 (17) |
| Number of Pages | 30 |
| Year | 2023 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 1424-8220 |
| 1424-8239 | |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/s23177360 |
| Web Address (URL) | https://www.mdpi.com/1424-8220/23/17/7360 |
| Abstract | The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature. |
| Keywords | LiDAR; point cloud; classification; buildings; vegetation; terrain; urban areas; deep learning; machine learning; geometric features |
| Article Publishing Charge (APC) Amount Paid | 2600.0 |
| Article Publishing Charge (APC) Funding | Other |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
| 461103. Deep learning | |
| Byline Affiliations | School of Surveying and Built Environment |
| Abdelmalek Essaâdi University, Morocco |
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https://research.usq.edu.au/item/z26z4/contribution-of-geometric-feature-analysis-for-deep-learning-classification-algorithms-of-urban-lidar-data
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