LAUSR

It is a key issue denoising airborne LiDAR bathymetry (ALB) full waveforms in extracting underwater topography. Empirical mode decomposition (EMD) is suitable for the nonlinear and nonstationary characteristics of ALB full waveforms and obviates the necessity for preset basis functions. Still, the direct discarding of noise-dominated intrinsic mode functions (IMFs) by traditional EMD leads to oversmoothing or undersmoothing of signals. In this letter, an EMD-based multiresolution analysis (EMD-MRA) method is suggested for ALB full-waveform denoising. This method utilizes the zero-padding technique based on the discrete cosine transform (DCT) to achieve the second-layer decomposition of the noise-dominant IMFs obtained in the first layer. Subsequently, Savitzky–Golay (S–G) filtering is employed for the noise-dominant IMFs from the second-layer decomposition. The intricate details embedded in the IMFs are meticulously extracted through a process of scaling the signal from a coarse to a fine resolution, ensuring the preservation of valuable information. The experiments, conducted using measurement data, demonstrate that the EMD-MRA method is effective in adaptively denoising the ALB full-waveform data, showcasing sufficient robustness. Compared with traditional EMD and wavelet threshold denoising (WTD) methods, the signal-to-noise ratio (SNR) of the denoising results using the proposed approach is improved by 6.769 and 0.971 dB in area a and by 19.672 and 5.317 dB in area b. The root mean square error (RMSE) is reduced by 0.455 and 0.971 in area a and by 0.979 and 0.47 in area b, effectively retaining the complex details of the original signal.