A Scanline-Based Sliding Window Filtering Method for UAV-Borne LiDAR Bathymetry Point Clouds.
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| Title: | A Scanline-Based Sliding Window Filtering Method for UAV-Borne LiDAR Bathymetry Point Clouds. |
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| Authors: | Yu, Jiayong1 (AUTHOR), Zhang, Jing1,2 (AUTHOR), Mu, Jiangchao2,3 (AUTHOR), Guo, Jiachun1 (AUTHOR), Lv, Deliang2,3 (AUTHOR) lvdeliang@siom.ac.cn, Du, Xiaoxue1,3 (AUTHOR), Lin, Peng1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1635. 21p. |
| Subjects: | LIDAR, Point cloud, Bathymetry, Signal denoising, Water depth, Data quality |
| Abstract: | Highlights: What are the main findings? A novel scanline-based sliding window filtering method is proposed for the denoising of UAV-borne LiDAR bathymetric point clouds, which can effectively separate noise while completely retaining detailed features of complex terrain in shallow-water areas. The method achieves ≥96% noise recall and F1-score ≥ 0.9 across different terrains, with excellent filtering performance and strong adaptability, significantly improving the quality of point cloud data. What are the implications of the main findings? This study innovatively applies bathymetric LiDAR scanline information to point cloud filtering, providing a new paradigm for UAV-borne LiDAR bathymetry data processing. The proposed method is of great reference significance for improving the data quality of UAV-borne LiDAR bathymetry and can effectively promote the application of this technology in complex shallow-water areas. To improve the data quality of underwater point clouds acquired by UAV-borne LiDAR bathymetry, a scanline-based sliding window filtering method is proposed based on an analysis of scanline data characteristics. Scanline data of underwater point clouds are first extracted from raw point clouds, and the radius outlier removal algorithm is employed to eliminate outliers. Taking the acquisition time of scanline points as the X-axis and elevation as the Y-axis, a 3D problem is simplified into a 2D representation, and a sliding window is constructed along the scanline. Robust least-squares fitting is applied within the window. The median absolute deviation of the fitting residuals is adopted to calculate the terrain feature values for quantifying the terrain complexity, followed by an adaptive filtering threshold determination according to terrain feature values. Fine filtering of the individual scanlines is performed using a point-by-point sliding window. Experimental results demonstrate that the proposed method is adaptable to various terrain conditions, achieving a noise recall rate ≥ 96%, an overall filtering accuracy ≥99%, and an F1-score ≥ 0.9. Particularly, the precision rate in flat-water areas reached 97.37%. Overall, the proposed filtering method effectively separates noise points while preserving detailed terrain features and supports UAV-borne LiDAR bathymetry for mapping complex shallow-water regions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel scanline-based sliding window filtering method is proposed for the denoising of UAV-borne LiDAR bathymetric point clouds, which can effectively separate noise while completely retaining detailed features of complex terrain in shallow-water areas. The method achieves ≥96% noise recall and F1-score ≥ 0.9 across different terrains, with excellent filtering performance and strong adaptability, significantly improving the quality of point cloud data. What are the implications of the main findings? This study innovatively applies bathymetric LiDAR scanline information to point cloud filtering, providing a new paradigm for UAV-borne LiDAR bathymetry data processing. The proposed method is of great reference significance for improving the data quality of UAV-borne LiDAR bathymetry and can effectively promote the application of this technology in complex shallow-water areas. To improve the data quality of underwater point clouds acquired by UAV-borne LiDAR bathymetry, a scanline-based sliding window filtering method is proposed based on an analysis of scanline data characteristics. Scanline data of underwater point clouds are first extracted from raw point clouds, and the radius outlier removal algorithm is employed to eliminate outliers. Taking the acquisition time of scanline points as the X-axis and elevation as the Y-axis, a 3D problem is simplified into a 2D representation, and a sliding window is constructed along the scanline. Robust least-squares fitting is applied within the window. The median absolute deviation of the fitting residuals is adopted to calculate the terrain feature values for quantifying the terrain complexity, followed by an adaptive filtering threshold determination according to terrain feature values. Fine filtering of the individual scanlines is performed using a point-by-point sliding window. Experimental results demonstrate that the proposed method is adaptable to various terrain conditions, achieving a noise recall rate ≥ 96%, an overall filtering accuracy ≥99%, and an F1-score ≥ 0.9. Particularly, the precision rate in flat-water areas reached 97.37%. Overall, the proposed filtering method effectively separates noise points while preserving detailed terrain features and supports UAV-borne LiDAR bathymetry for mapping complex shallow-water regions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101635 |
