A novel noise reduction method for space-borne full waveforms based on empirical mode decomposition

Abstract

In this project, a novel noise reduction method based on empirical mode decomposition (EMD), referred to as EMD-AdaptiveP, is proposed and investigated with the expectation of providing an effective enhancement for the full waveform of nonlinear and nonstationary signals obtained by space-borne laser altimetry and the forthcoming GaoFen-7 (GF-7) mapping satellite in China. This method combines EMD with a similar strategy of wavelet filtering to adapt to the characteristics of each waveform. The reconstruction of an effective waveform signal is implemented through reverse superimposition of its intrinsic mode functions (IMFs) and the residual, which is thresholded by the three-sigma principle of background noise of the waveform signal and an additional check mechanism in the lower or higher signal of noise levels, after the waveform is decomposed by EMD. This method is qualitatively and quantitatively examined with simulated waveform data, real ice, cloud and land elevation satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) waveforms and GF-7 proto-waveform data, in contrast with other IMF selection schemes, like EMD-soft, EMD-hard and EMD-Wavelet, or the traditional filtering methods, such as Gaussian, μ\\λ and wavelet. The results show that the method of EMD-AdaptiveP (1) is sufficiently robust to denoise the waveform signal with different levels of signal-to-noise ratio, (2) has the adaptive ability to distinguish effective signal IMFs from noise IMFs in signal reconstruction, and (3) performs noise reduction more effectively than traditional methods.