Multiscale Quadtree for Denoising Spaceborne Photon-counting LiDAR

Zhang Baichuan; Dong Zhipeng; Liu Yanxiong; Yang Fanlin; Chen Yilan; Li Jie

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Zhang Baichuan，Dong Zhipeng，Liu Yanxiong, et al. Multiscale Quadtree for Denoising Spaceborne Photon-counting LiDAR[J]. Haiyang Xuebao，2025, 47(x)：1–14

Citation:

Zhang Baichuan，Dong Zhipeng，Liu Yanxiong, et al. Multiscale Quadtree for Denoising Spaceborne Photon-counting LiDAR[J]. Haiyang Xuebao，2025, 47(x)：1–14

Citation:

Zhang Baichuan，Dong Zhipeng，Liu Yanxiong, et al. Multiscale Quadtree for Denoising Spaceborne Photon-counting LiDAR[J]. Haiyang Xuebao，2025, 47(x)：1–14

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Multiscale Quadtree for Denoising Spaceborne Photon-counting LiDAR

Zhang Baichuan^{1, 2, 3},
Dong Zhipeng^{1, 2, 3
,
,},
Liu Yanxiong^{1, 2, 3},
Yang Fanlin^{1, 3},
Chen Yilan^{2, 3},
Li Jie^{2, 3}

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
3.
The Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China

Received Date: 2024-08-21
Rev Recd Date: 2025-02-21

Available Online: 2025-04-14

Abstract

Abstract

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. However, due to the influence of laser, atmospheric scattering and other factors, ICESat-2 data contains a lot of noise. Combining multiscale analysis with the quadtree algorithm, we propose a new photon-counting LiDAR denoising method to discard the large amount of noise in ICESat-2 data. First, Kernel Density Estimation (KDE) is performed using a Gaussian kernel function and the K-fold cross validation to set threshold values that separate sea surface photons from seafloor photons. Second, abnormal photons are removed using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with adaptive parameters, yielding rough denoising results. Finally, for the seafloor photon partition window, accurate seafloor signal photons are extracted across multiple scales using the pre-judgment quadtree. The study used ICESat-2 photon-counting data from typical islands and reefs, comparing it with in situ water depth measurements. The coefficient of determination (R²) in the study area reaches 95% and 98%, with root mean square errors (RMSE) of 1.01 m and 0.77 m, respectively. The results show that the proposed method can accurately extract underwater topographic information, providing a solid foundation for the inversion of shallow marine topography.
- bathymetry,
- ICESat-2,
- photon classification,
- kernel density estimation,
- multiscale analysis,
- quadtree

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References(34)

References

[1]	杨元喜, 徐天河, 薛树强. 我国海洋大地测量基准与海洋导航技术研究进展与展望[J]. 测绘学报, 2017, 46(1): 1−8. doi: 10.11947/j.AGCS.2017.20160519 Yang Yuanxi, Xu Tianhe, Xue Shuqiang. Progresses and prospects in developing marine geodetic datum and marine navigation of China[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 1−8. doi: 10.11947/j.AGCS.2017.20160519
[2]	马毅, 张杰, 张靖宇, 等. 浅海水深光学遥感研究进展[J]. 海洋科学进展, 2018, 36(3): 331−351. doi: 10.3969/j.issn.1671-6647.2018.03.001 Ma Yi, Zhang Jie, Zhang Jingyu, et al. Progress in shallow water depth mapping from optical remote sensing[J]. Advances in Marine Science, 2018, 36(3): 331−351. doi: 10.3969/j.issn.1671-6647.2018.03.001
[3]	Hodúl M, Bird S, Knudby A, et al. Satellite derived photogrammetric bathymetry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142: 268−277. doi: 10.1016/j.isprsjprs.2018.06.015
[4]	Albright A, Glennie C. Nearshore bathymetry from fusion of Sentinel-2 and ICESat-2 observations[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): 900−904. doi: 10.1109/LGRS.2020.2987778
[5]	Caballero I, Stumpf R P. Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters[J]. Estuarine, Coastal and Shelf Science, 2019, 226: 106277. doi: 10.1016/j.ecss.2019.106277
[6]	Ranndal H, Sigaard Christiansen P, Kliving P, et al. Evaluation of a statistical approach for extracting shallow water bathymetry signals from ICESat-2 ATL03 photon data[J]. Remote Sensing, 2021, 13(17): 3548. doi: 10.3390/rs13173548
[7]	Parrish C E, Magruder L A, Neuenschwander A L, et al. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance[J]. Remote sensing, 2019, 11(14): 1634. doi: 10.3390/rs11141634
[8]	朱笑笑, 王成, 习晓环, 等. ICESat-2星载光子计数激光雷达数据处理与应用研究进展[J]. 红外与激光工程, 2020, 49(11): 20200259. doi: 10.3788/IRLA20200259 Zhu Xiaoxiao, Wang Cheng, Xi Xiaohuan, et al. Research progress of ICESat-2/ATLAS data processing and applications[J]. Infrared and Laser Engineering, 2020, 49(11): 20200259. doi: 10.3788/IRLA20200259
[9]	方勇, 曹彬才, 高力, 等. 激光雷达测绘卫星发展及应用[J]. 红外与激光工程, 2020, 49(11): 20201044. doi: 10.3788/IRLA20201044 Fang Yong, Cao Bincai, Gao Li, et al. Development and application of lidar mapping satellite[J]. Infrared and Laser Engineering, 2020, 49(11): 20201044. doi: 10.3788/IRLA20201044
[10]	焦慧慧, 谢俊峰, 刘仁, 等. 星载对地观测光子计数激光雷达去噪方法浅析[J]. 航天返回与遥感, 2021, 42(5): 140−150. doi: 10.3969/j.issn.1009-8518.2021.05.015 Jiao Huihui, Xie Junfeng, Liu Ren, et al. Discussion on denoising method of photon counting LiDAR for satellite ground observation[J]. Spacecraft Recovery & Remote Sensing, 2021, 42(5): 140−150. doi: 10.3969/j.issn.1009-8518.2021.05.015
[11]	. Magruder L A, Wharton M E, Stout K D, et al. Noise filtering techniques for photon-counting ladar data[C]//Proceedings of SPIE the International Society for Optical Engineering. Baltimore: SPIE, 2012: 237−245.
[12]	Herzfeld U C, McDonald B W, Wallin B F, et al. Algorithm for detection of ground and canopy cover in micropulse photon-counting lidar altimeter data in preparation for the ICESat-2 mission[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 2109−2125.
[13]	Xie Huan, Xu Qi, Ye Dan, et al. A comparison and review of surface detection methods using MBL, MABEL, and ICESat-2 photon-counting laser altimetry data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 7604−7623.
[14]	Xie Huan, Ye Dan, Xu Qi, et al. A density-based adaptive ground and canopy detecting method for ICESat-2 photon-counting data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4411813.
[15]	. Zhang Jiashu, Kerekes J, Csatho B, et al. A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data[C]//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City: IEEE, 2014: 177−180.
[16]	谢锋, 杨贵, 舒嵘, 等. 方向自适应的光子计数激光雷达滤波方法[J]. 红外与毫米波学报, 2017, 36(1): 107−113. doi: 10.11972/j.issn.1001-9014.2017.01.019 Xie Feng, Yang Gui, Shu Rong, et al. An adaptive directional filter for photon counting Lidar point cloud data[J]. Journal of Infrared and Millimeter Waves, 2017, 36(1): 107−113. doi: 10.11972/j.issn.1001-9014.2017.01.019
[17]	Chen Bowei, Pang Yong, Li Zengyuan, et al. Ground and top of canopy extraction from photon-counting LiDAR data using local outlier factor with ellipse searching area[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1447−1451. doi: 10.1109/LGRS.2019.2899011
[18]	Ma Yue, Xu Nan, Sun Jinyan, et al. Estimating water levels and volumes of lakes dated back to the 1980s using Landsat imagery and photon-counting lidar datasets[J]. Remote Sensing of Environment, 2019, 232: 111287. doi: 10.1016/j.rse.2019.111287
[19]	Ma Yue, Zhang Wenhao, Sun Jinyan, et al. Photon-counting lidar: an adaptive signal detection method for different land cover types in coastal areas[J]. Remote Sensing, 2019, 11(4): 471. doi: 10.3390/rs11040471
[20]	Zhu Xiaoxiao, Nie Sheng, Wang Cheng, et al. A noise removal algorithm based on OPTICS for photon-counting LiDAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(8): 1471−1475. doi: 10.1109/LGRS.2020.3003191
[21]	Zhang Guoping, Xu Qing, Xing Shuai, et al. A noise-removal algorithm without input parameters based on quadtree isolation for photon-counting LiDAR[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 6501905.
[22]	Huang Guoan, Dong Zhipeng, Liu Yanxiong, et al. An optimized denoising method for ICESat-2 photon-counting data considering heterogeneous density and weak connectivity[J]. Optics Express, 2023, 31(25): 41496−41517. doi: 10.1364/OE.502934
[23]	Song Yue, Ma Yue, Zhou Zhibiao, et al. Signal photon extraction and classification for ICESat-2 photon-counting lidar in coastal areas[J]. Remote Sensing, 2024, 16(7): 1127. doi: 10.3390/rs16071127
[24]	Xie Huan, Xu Qi, Luan Kuifeng, et al. Evaluating ICESat-2 seafloor photons by underwater light-beam propagation and noise modeling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 4203018.
[25]	. Davis R A, Lii K S, Politis D N. Remarks on some nonparametric estimates of a density function[M]//Davis R A, Lii K S, Politis D N. Selected Works of Murray Rosenblatt. New York: Springer, 2011: 95−100.
[26]	Parzen E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065−1076. doi: 10.1214/aoms/1177704472
[27]	. Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226−231.
[28]	Finkel R A, Bentley J L. Quad trees a data structure for retrieval on composite keys[J]. Acta Informatica, 1974, 4(1): 1−9. doi: 10.1007/BF00288933
[29]	Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62−66. doi: 10.1109/TSMC.1979.4310076
[30]	Zheng Xuebo, Hou Chunping, Huang Meiyan, et al. A density and distance-based method for ICESat-2 photon-counting data denoising[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 6500405.
[31]	Egbert G D, Erofeeva S Y. Efficient inverse modeling of barotropic ocean tides[J]. Journal of Atmospheric and Oceanic technology, 2002, 19(2): 183−204. doi: 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2
[32]	Bargaoui Z K, Chebbi A. Comparison of two kriging interpolation methods applied to spatiotemporal rainfall[J]. Journal of Hydrology, 2009, 365(1/2): 56−73.
[33]	. Parrish C E, Magruder L A, Neuenschwander A L, et al. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance[J]. Remote sensing, 2019, 11(14): 1634. (查阅网上资料, 该文献与第7条文献重复, 请确认)
[34]	孟文君, 李杰, 张凯, 等. 基于改进DBSCAN算法的ICESat-2海面点数据去噪处理及精度评估[J]. 海洋通报, 2021, 40(6): 675−682. doi: 10.11840/j.issn.1001-6392.2021.06.008 Meng Wenjun, Li Jie, Zhang Kai, et al. De-noising and accuracy evaluation of ICESAT-2 sea surface data based on DBSCAN algorithm[J]. Marine Science Bulletin, 2021, 40(6): 675−682. doi: 10.11840/j.issn.1001-6392.2021.06.008