Denoising of Photon-Counting LiDAR Bathymetry Based on Adaptive Variable OPTICS Model and Its Accuracy Assessment

Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering struct...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 16; no. 18; p. 3438
Main Authors Li, Peize, Xu, Yangrui, Zhao, Yanpeng, Liang, Kun, Si, Yuanjie
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Accuracy
Adaptive optics
adaptive variable OPTICS
Algorithms
Analysis
Background noise
Bathymeters
bathymetric method
Bathymetry
Clustering
Environmental protection
ICESat-2/ATLAS
Lasers
Lidar
Methods
Noise reduction
Optical radar
Optics
Parameter identification
photon-counting LiDAR
Photons
Remote sensing
Shallow water
Variables
Water
United States > US
Puerto Rico
Culebra
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Summary:Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher F1-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average MAE of 0.28 m and RMSE of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16183438