Denoising for satellite laser altimetry full-waveform data based on EMD-Hurst analysis

Full-waveform decomposition is crucial for obtaining accurate satellite-ground distance, the accuracy of which is severely affected by noises. However, the traditional filters all depend on filtering parameters. This paper presents a new and adaptive method for denoising based on empirical mode decomposition (EMD) and Hurst analysis (EMD-Hurst). The noisy full-waveforms are first decomposed into their intrinsic mode functions (IMFs), and the Hurst exponent of each IMF is established by the detrended fluctuation analysis. The IMF is regarded as the high-frequency noise and is deleted if its Hurst exponent is <= 0.5. Both simulated and real full-waveforms were conducted to validate and evaluate the method by comparing with six other IMF selection methods via metrics like waveform decomposition consistency ratio (CR), average error of decomposition parameters, and ICESat/GLAS waveform-parameter product GLAH05. The comparisons show that: (1) under different SNR conditions, EMD-Hurst performs robustly and obtains a higher CR than other EMD based methods; (2) obtains the highest average CR and a relatively lower average error for the echo parameters; and (3) peak numbers and fitting accuracy for GLAH01 are more reasonable and precise than those of GLAH05, which could offer a good reference for the processing on future space-borne full-waveform data.