Full-waveform decomposition is crucial for obtaining accurate satelliteground distance,the accuracy of which is severely affected by noises.However,the traditional filters all depend on filtering parameters.This pape...Full-waveform decomposition is crucial for obtaining accurate satelliteground 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 highfrequency 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 waveformparameter 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.展开更多
基金the National Natural Science Foundation of China,under[grant numbers 41822106 and 41571407]China High-resolution Earth Observation System,under[grant number 11-Y20A12-9001-17/18]+3 种基金the Science and Technology Innovation Action Plan of Shanghai,under[grant number 18511102100]the Dawn Program of Shanghai Education Commission,China,under[grant number 18SG22]State Key Laboratory of Disaster Reduction in Civil Engineering,under[grant number SLDRCE19-B-35]the Fundamental Research Funds for the Central Universities of China.
文摘Full-waveform decomposition is crucial for obtaining accurate satelliteground 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 highfrequency 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 waveformparameter 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.
