Adaptive Wavelet Threshold Denoising for Bathymetric Laser Full-Waveforms With Weak Bottom Returns
Xinglei Zhao, Hui Xia, Jianhu Zhao, Fengnian Zhou
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.