Waveform decomposition and feature extraction of airborne LiDAR bathymetry

Liu, Jiaoyang; Su, Dianpeng; Chen, Qi; Yang, Anxiu; Wang, Xiankun; Yang, Fanlin

doi:10.1109/igarss47720.2021.9553870

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al proposed an underwater decomposition and feature extraction based on airborne Lidar; it is considered that this can provide a basis for research on coastline separation, complex shallow-water-area sounding, water and underwater integrated splicing, and seabed classification, etc. [12]. Schwarz deeply analyzed the process of using Lidar to measure underwater depth, and highlighted the troubles and importance of this process [13].…”

Section: Introductionmentioning

confidence: 99%

Method to Solve Underwater Laser Weak Waves and Superimposed Waves

Kang

Lin

et al. 2023

Sensors

View full text Add to dashboard Cite

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg–Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

show abstract

Section: Introductionmentioning

confidence: 99%