A new extraction and grading method for underwater topographic photons of photon-counting LiDAR with different observation conditions

ABSTRACT

Spaceborne photon-counting light detection and ranging (LiDAR) have been extensively applied in shallow-water bathymetry. The density of underwater topographic photons (UTP) varies and is discontinuous due to sunlight noise, beam intensity, and seabed reflectivity, which differ from the land photon distribution due to the attenuation of water. Therefore, a general method for extracting and grading UTP is still lacking. We propose an active contour method combined with a variable convolution kernel method to calculate the photon range by considering the energy contributions of adjacent photons. Adaptive parameters under different observation conditions were determined to obtain the optimal convolution kernel using a kernel ridge regression model. This implies that the number of photons contained in the buffer zone was largest after the extracted UTP was fitted to a curve. Quantitative and qualitative verifications proved that the method performed well under different conditions. The photons obtained by the energy functional and the curve obtained by the fitting method were then used to grade the photons. Finally, an online developed UTP dataset and extraction framework were proposed to provide an applicable method for current and subsequent spaceborne photon-counting LiDAR.

KEYWORDS:

• Photon-counting LiDAR
• photons denoising
• underwater topographic photons
• active contours
• kernel ridge regression
• ICESat-2

Acknowledgements

We are grateful to the NSIDC for providing the ATL03 data, and thanks to ATL03 data for their contribution to the related study area. We also would like to thank the editors and reviewers for their valuable comments on the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available in an online website, at http://www.oceanread.com:5600/ExPress/ExDataPresentation. The results supporting the paper are available at https://github.com/iwzhcode/ACVCK, and the code will be available at this site.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers, 41930535, 62071279, 41871382, 42301501 and 42001416), and the Independent Research Program of Key Laboratory of Land Satellite Remote Sensing Application, MNR (grant number BN2302-6).