سابِق|تال

Denoising method for light weight photon counting LiDAR based on an improved local sparse coefficient

  • role： First author第一作者
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

    Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China

  • Email：kfluan@shou.edu.cn
  • Introduction：栾奎峰，研究方向为新型激光成像质量控制理论和应用。E-mail： kfluan@shou.edu.cn
LUAN Kuifeng12,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

ZHANG Kunning1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

    Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China

  • Email：zgqiu@shou.edu.cn
  • Introduction：邱振戈，研究方向为海洋测绘与遥感。E-mail：zgqiu@shou.edu.cn
QIU Zhenge12*,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

WANG Jie1,  
  • Affiliation：

    College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

WANG Zhenhua3,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

XUE Ye1,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

    Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China

ZHU Weidong12,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

LING Dandan1,  
  • Affiliation：

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

ZHAO Xueyan1

ملخص

The photon counting LiDAR bathymetry system carried by UAVs is an important method for island reef mapping and shallow water bathymetry due to the characteristics of high detection sensitivity and high density. However, the high detection sensitivity also leads to the acquired photonic point cloud data with large background noise, a strong correlation between the signal-to-noise ratio and the type of ground objects, and large differences in the density distribution of photons, and the existing denoising algorithms cannot be well applied.In this paper, a denoising method for raw photon observation data is proposed. First, the effective signal interval of the raw photon observation data is calculated based on the histogram statistics method, and then the data in the interval are coarsely denoised by the grid statistics method. Finally, the local sparse coefficient method is improved, the horizontal ellipse search is used to calculate the local sparse coefficient value of each photon data in the grid, and the method of maximum interclass variance is introduced to determine the separation threshold of noise photons and signal photons, which improves the original photon observation data. Denoising accuracy. Jiajing Island and the adjacent shallow sea terrain in Hainan Province are selected as the research area to verify the denoising algorithm proposed.The results show that the average F1-score in the high signal-to-noise ratio areas, such as the island vegetation coverage area and the sandy intertidal zone, reaches 94.64% and 98.96%, respectively, and the average F1-score in the low signal-to-noise ratio area, such as the shallower and deeper water bodies near the coast, can also reach 93.04% and 90.74%, respectively. The overall F1-score is 94.34%, which can effectively remove most of the noise points and has strong adaptability to island vegetation, sandy land and underwater terrain of different depths with different signal-to-noise ratios.In addition, this paper also selects the spaceborne ICESat-2 photon dataset of coral islands in the South China Sea, which further verifies the availability and applicability of the denoising algorithm proposed in this paper on spaceborne photonic point cloud data.

مفهوم

remote sensing;photon counting;UAV;lidar;local sparse coefficient;OTSU;noise removal

References

  1. 1.
    Agyemang M. 2002. Local Sparsity Coefficient-Based Mining of Outliers. Windsor, Ontario: University of Windsor
  2. 2.
    Agyemang M. 2004. LSC-mine: algorithm for mining local outliers//IRMA International Conference. Hershey: IRM Press: 5-8
  3. 3.
    Chen Y F, Le Y, Zhang D F, Wang Y, Qiu Z G and Wang L Z. 2021. A photon-counting LiDAR bathymetric method based on adaptive variable ellipse filtering. Remote Sensing of Environment, 256: 112326
  4. 4.
    Duan X J. 2017. Imaging Technology Based on Time-Correlated Single Photon Counting. Xi’an: Xidian University
  5. 5.
    Fouche D G. 2003. Detection and false-alarm probabilities for laser radars that use Geiger-mode detectors. Applied Optics, 42(27): 5388-5398
  6. 6.
    Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 20(5): 1138-1150
  7. 7.
    Liu H, Chen P, Mao Z H and Pan D L. 2020. Iterative retrieval method for ocean attenuation profiles measured by airborne lidar. Applied Optics, 59(10): C42-C51
  8. 8.
    Liu S, Luan K F, Tan K and Zhang W G. 2021. Multi-type vegetation coverage tidal flat terrain filtering based on UAV LiDAR point cloud. Remote Sensing Technology and Application, 36(6): 1272-1283
  9. 9.
    Ma Y, Liu R, Li S, Zhang W H, Yang F L and Su D P. 2018. Detecting the ocean surface from the raw data of the MABEL photon-counting lidar. Optics Express, 26(19): 24752-24762
  10. 10.
    Markus T, Neumann T, Martino A, Abdalati W, Brunt K, Csatho B, Farrell S, Fricker H, Gardner A, Harding D, Jasinski M, Kwok R, Magruder L, Lubin D, Luthcke S, Morison J, Nelson R, Neuenschwander A, Palm S, Popescu S, Shum C K, Schutz B E, Smith B, Yang Y K and Zwally J. 2017. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sensing of Environment, 190: 260-273
  11. 11.
    Neumann T, Brenner A, Hancock D, Robbins J, Saba J, Harbeck K and Gibbons A. 2019. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) Project: Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons ATL03. National Aeronautics and Space Administration, Goddard Space Flight Center. Available online: .
  12. 12.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  13. 13.
    Popescu S C, Zhou T, Nelson R, Neuenschwander A, Sheridan R, Narine L and Walsh K M. 2018. Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sensing of Environment, 208: 154-170
  14. 14.
    Shen G Y, Zheng T X, Li Z H, Wu E, Yang L, Tao Y L, Wang C H and Wu G. 2021. High-speed airborne single-photon LiDAR with GHz-gated single-photon detector at 1550 nm. Optics and Laser Technology, 141: 107109
  15. 15.
    Shim H and Lee S. 2014. Hybrid exposure for depth imaging of a time-of-flight depth sensor. Optics Express, 22(11): 13393-13402
  16. 16.
    Wang C S, Li Q Q, Liu Y X, Wu G F, Liu P and Ding X L. 2015. A comparison of waveform processing algorithms for single-wavelength LiDAR bathymetry. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 22-35
  17. 17.
    Wang X, Glennie C and Pan Z G. 2017. An adaptive ellipsoid searching filter for airborne single-photon lidar. IEEE Geoscience and Remote Sensing Letters, 14(8): 1258-1262
  18. 18.
    Wu Z S and Liu A N. 2002. Scattering of solar and atmospheric background radiation from a target. International Journal of Infrared and Millimeter Waves, 23(6): 907-917
  19. 19.
    Xia S B, Wang C, Xi X H, Luo S Z and Zeng H C. 2014. Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2. Journal of Remote Sensing, 18(6): 1199-1207
  20. 20.
    Xie H, Ye D, Xu Q, Sun Y, Huang P Q, Tong X H, Guo Y L, Liu X S and Liu S J. 2022. A density-based adaptive ground and canopy detecting method for ICESat-2 photon-counting data. IEEE Transactions on Geoscience and Remote Sensing, 60: 4411813
  21. 21.
    Yang B S, Xu W X and Yao W. 2014. Extracting buildings from airborne laser scanning point clouds using a marked point process. GIScience and Remote Sensing, 51(5): 555-574
  22. 22.
    Zhang H H, Ding Y X and Huang G H. 2019. Photon counting laser bathymetry system. Infrared and Laser Engineering, 48(1): 0106002
  23. 23.
    Zhang J S and Kerekes J. 2015. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data. IEEE Geoscience and Remote Sensing Letters, 12(4): 726-730
  24. 24.
    Zhu X X, Nie S, Wang C, Xi X H, Wang J S, Li D and Zhou H Y. 2021. A noise removal algorithm based on OPTICS for photon-counting LiDAR data. IEEE Geoscience and Remote Sensing Letters, 18(8): 1471-1475

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