Abstract
Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.
Highlights
Ground-based lidar is an effective remote sensing tool in atmosphere remote sensing(Kovalev and Eichinger, 2004)
The Fourier transform (FT) cannot work well when dealing with lidar signal, the empirical mode decomposition (EMD) results in errors when encounter with lidar signal that has huge signal fluctuation(Tian et al, 2014), while the wavelet transforms (WTs) have to select a proper wavelet base to adapt different signals(Fang et al, 2005)
We introduced a universal de-noising method for ground-based lidar signal based on signal segmentation and reconstruction
Summary
Ground-based lidar is an effective remote sensing tool in atmosphere remote sensing(Kovalev and Eichinger, 2004). To reduce the noise level, many methods have been adopted. The low-pass filters are mostly accepted de-noising methods. The other low-pass filters, including Fourier transform (FT), empirical mode decomposition (EMD), and wavelet transforms (WTs), are widely used in signal de-noising(Rabiner and Gold, 1975, Huang et al, 1998, Flandrin et al, 2004, Mallat and Hwang, 1992). There is no universal de-noising method that can fit all lidar signals. We introduced a universal de-noising method for ground-based lidar signal based on signal segmentation and reconstruction. Considering the variation of lidar signal caused by weather and other influences, we segment the signal into different parts, which can be processed by different de-noising method based on their own characteristics. The simulation signal and real lidar signal experiments are introduced and discussed to show the feasibility of the proposed method
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