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- W4205289332 abstract "Wavelet threshold denoising with different threshold selection rules (TSRs) were used to reduce random noise (RN) in bathymetric laser full-waveforms. A nonreasonable threshold used for denoising can result in over-smoothing or under-smoothing of the signal and easily remove details of weak bottom return (BR). A unique and optimal TSR for all bathymetric full-waveforms of waters with different depths or turbidities is unavailable. Hence, an adaptive threshold selection (ATS) is proposed to improve the performance of RN reduction by adaptively selecting a threshold for each full-waveform based on the prominence of BR-to-noise ratio. The proposed method is applied to reduce the RN in raw green laser full-waveforms collected via Optech coastal zone mapping and imaging LIght Detection And Ranging (LiDAR). Compared with other traditional methods, the ATS improves the ratio of detectable BR by 5.64% and achieves a root mean squared error (RMSE) closer to the real RN level. Therefore, ATS can effectively remove the RN, enhance the prominence, and ensure the fidelity of weak BR." @default.
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- W4205289332 date "2022-01-01" @default.
- W4205289332 modified "2023-09-30" @default.
- W4205289332 title "Adaptive Wavelet Threshold Denoising for Bathymetric Laser Full-Waveforms With Weak Bottom Returns" @default.
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- W4205289332 doi "https://doi.org/10.1109/lgrs.2022.3141057" @default.
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