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- Title
An Optimal Denoising Method for Spaceborne Photon-Counting LiDAR Based on a Multiscale Quadtree.
- Authors
Zhang, Baichuan; Liu, Yanxiong; Dong, Zhipeng; Li, Jie; Chen, Yilan; Tang, Qiuhua; Huang, Guoan; Tao, Junlin
- Abstract
Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry.
- Subjects
PHOTON counting; PROBABILITY density function; STANDARD deviations; UNDERWATER noise; LIDAR; WATER depth
- Publication
Remote Sensing, 2024, Vol 16, Issue 13, p2475
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16132475