Abstract
The study aims to address the multiple pseudo-planes and low efficiency of random sample consensus (RANSAC) while extracting building planes from airborne light detection and ranging (LiDAR) data. A Naive Bayes–RANSAC method is proposed for the proportional segmentation of quantitative features. First, the point cloud is divided into ground and non-ground points using the cloth simulation filtering (CSF) algorithm. RANSAC is used to extract the non-ground point directly. Accordingly, several planes are obtained as the original building roofs. The differences in the density and echo features between the pseudo-plane and the training samples’ building plane are then analyzed. The percentage of the points whose densities are greater than the threshold, determined by the densities of the building edges, is calculated within each plane. Similarly, the percentage of the points whose echo numbers are higher than one is calculated within each plane. The two percentages calculated above are used as the classification basis of Naive Bayes. Finally, the building roofs can be extracted with the Naive Bayes classifier. This method can extract building roofs from LiDAR point clouds without external data. Two experiments are conducted to reveal the method’s practicality and reliability. The results demonstrate completeness of 89.0% and correctness of 94.0% at the per-area level for the first experiment and 90.7% and 91.7% for the second experiment. Simultaneously, the second experiment exhibits superior results compared with other methods under the same conditions.
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Acknowledgements
We would like to thank the CloudCompare (https://www.cloudcompare.org/) for data processing and mapping. And this work is based on [data, processing] services provided by the OpenTopography Facility with support from the National Science Foundation under NSF Award Numbers 1557484, 1557319, and 1557330.
Funding
This work was funded by National Natural Science Foundation of China (NO.41971310 and NO.41930109); The Natural Science Foundation of Tianjin City (No.18JCYBJC90700); Scientific research plan projects of Tianjin education commission, Grant number 2017KJ061; Beijing Outstanding Young Scientist Program(BJJWZYJH01201910028032).
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HaotongWang, GuangyaoDuan and ZhenghuiYi conceived and designed the study. HaotongWang and ZhenWang processed point cloud data and experimented. HaotongWang and GuangyaoDuan wrote the paper.
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Yi, Z., Wang, H., Duan, G. et al. An Airborne LiDAR Building-Extraction Method Based on the Naive Bayes–RANSAC Method for Proportional Segmentation of Quantitative Features. J Indian Soc Remote Sens 49, 393–404 (2021). https://doi.org/10.1007/s12524-020-01222-4
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DOI: https://doi.org/10.1007/s12524-020-01222-4
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An Airborne LiDAR Building-Extraction Method Based on the Naive Bayes–RANSAC Method for Proportional Segmentation of Quantitative Features- Published:
- 01 February 2021
- Received:
- 03 September 2019
- Accepted:
- 21 October 2020
DOI: https://doi.org/10.1007/s12524-020-01222-4