International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
ATMOSPHERIC LIDAR NOISE REDUCTION BASED ON ENSEMBLE EMPIRICAL
MODE DECOMPOSITION
Jun LI**, Wei GONG*, Yingying Ma“
? LIESMARS, Wuhan University, Wuhan 430079, China - larkiner@gmail.com
Commission VIII/3
KEY WORDS: LIDAR; Analysis; Algorithms; Atmosphere; Detection
ABSTRACT:
As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties. But
the effective range and data reliability are often limited by various noises. Performing a proper denoising method will improve the
quality of the signals obtained. The denoising method based on ensemble empirical mode decomposition (EEMD) is introduced,
but the denoised results are difficult to evaluated. A dual field-of-view lidar for observing atmospheric aerosols is described. The
backscattering signals obtained from two channels have different signal-to-noise ratios (SNR). To overcome the drawback of the
simulation experiment, the performance of noise reduction can be investigated by comparing the high SNR signal and the denoised
low SNR signal. With this approach, some parameters of the denoising method based on EEMD can be determined effectively. The
experimental results show that the EEMD-based method with proper parameters can effectively increase the atmospheric lidar
observing ability.
1. INTRODUCTION
1.1 General Instructions
Aerosol can directly affect climate change by scattering and
absorption of solar and other radiation, and also indirectly
affect the radiation by affecting cloud formation. As an active
remote sensing instrument, lidar provides a high spatial
resolution vertical profile of aerosol optical properties!!! But
the effective range and data reliability are often limited by
various noises. Unfortunately, the lidar data inversion is
sensitive to the lidar data at a far distance, which are under low
signal-to-noise ratio conditions. Performing a proper denoising
method will improve the quality of the signals obtained.
The measured lidar signal contains the laser backscattering
signal from aerosol and various noises. It can be expressed
simply as
Vased (1) 7 V(r) * N,G)- N,() (1)
where Vneasured (r) = signal actually measured
V(r) = signal from aerosol backscattering
Ni(r) = noise due to background light
Ne(r) = noise due to dark current and read out
electronics.
N» and N, can be statistically estimated by the signal obtained
from a very far distance where the laser backscattering signal
1s negligible.
* Corresponding author. Jun LI, larkiner@gmail.com
The power of the received signal typically falls with an
increase in range, but noise is usually considered as Gaussian
white noise, which is stable with range. The signal-to-noise
ratio (SNR) falls as the range increases, and the solution for
the lidar equation becomes unstable and even fails because of
the negative value produced by noise. So the signal must be
denoised before data retrieval for the aerosol properties.
There are several signal analysis methods widely adopted for
the noise reduction in the lidar signal. Most lidar systems
employ the multiple pulses averaging to enhance SNR. This
method can be considered as a low pass filtering process at the
cost of temporal resolution, high frequency backscattering
signal is also smoothed. Wavelet analysis is developed rapidly
as an effective tool for noise reduction”. A main drawback of
the wavelet analysis is that the basis functions are fixed, and
no such a basis function is proposed to correspond with the
features of lidar signals currently. The selection of the best
basis function is also a hard work.
2. DENOISING METHOD
2.1 Empirical mode decomposition
Huang et al. introduced the empirical mode decomposition
(EMD) for analyzing signals from non-stationary and non-
linear processes in 1998. The EMD method is proved to
address completeness, orthogonality, locality, and adaptivity
which are necessary to describe non-stationary and non-linear
processes. The major advantage of the EMD is posteriori
adaptive, because the basis functions are derived from the
