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Fig. 2. Near and far range signal
4. RESULT AND DISCUSSION
The performances of noise reduction are evaluated by overall
signal-to-noise ratio (SNR).
SNR =10*10 3 ar) (3)
g y y
S [ denoised (r) far (NT
where SNR = overall signal-to-noise ratio
Vg = far range signal intensity
Vaenoised = denoised near range signal intensity
Three pairs data show in Fig.2 are analyzed. ‘Near’ is
considered as original SNR. *R2' and *R3' are EMD-based
denoised results by subtracting the first two or three IMFs.
‘wavelet’ is the wavelet-based denoised result. ‘EEMD’ is
EEMD-based result. Firstly, the SNR of the original signal is
relatively low, the SNRs are less than 27, and denoised results
by most method are more than 29. Secondly, SNRs of the
‘EEMD' are better than ‘EMD’ and ‘wavelet’ method. Finally,
the ‘EMD’ results in Aug. 5 is even less than original data
because the aerosols of lower atmosphere changed rapidly in
that day. But ‘EEMD’ method is still effective.
20:10 20:12 01:40
Aug.3 Aug. 3 Aug.5
Near 24.03 24.03 26.57
R2 29.54 30.56 26.18
R3 29.65 29.44 8.59
wavelet 29.26 30.13 30.14
EEMD 30.22 31.73 30.29
Table 1. SNR of different denoising algorithms
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
5. CONCLUSIONS
The primary result shows that the EEMD-based method can
effectively increase the lidar observing ability. The result is
promising and further work is required to evaluated the
performance of noise reduction in different lidar system and
atmospheric environment.
REFERENCES
N. E. Huang, Z. Wu, 2008. A review on Hilbert-Huang
transform: Method and its applications to geophysical studies,
Rev. Geophys. 46, RG2006 .
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q.
Zheng, N. C. Yen, C. C. Tung, H. H. Liu, 1998. The empirical
mode decomposition and the Hilbert spectrum for nonlinear
and non-stationary time series analysis, Proc. Roy. Soc. Lond.
A 454, 903.
W. Gong, J. Li, F. Y. Mao, J. Y. Zhang, 2011. Comparison of
simultaneous signals obtained from a dual-field-of-view lidar
and its application to noise reduction based on empirical mode
decomposition, Chin. Opt. Lett. 9, 5,050101.
Z. Wu and N. E. Huang, 2009. Ensemble empirical mode
decomposition: a noise assisted data analysis method. Advance
in adaptive data analysis. 1,1.
S. H. Wu, Z. S. Liu, B. Y. Liu, 2006, Enhancement of lidar
backscatters signal-to-noise ratio using empirical mode
decomposition method, Opt. Commun. 267, 137.
J. Harms, 1979, Lidar return signals for coaxial and non-
coaxial systems with central obstruction, Appl. Opt. 18, 1559
A. O. Boudraa, J. C. Cexus, Z. Saidi, 2004, EMD-based signal
noise reduction, Int. J. Signal Process. 1, 33.
ACKNOWLEDGEMENTS
This work was supported by 973 Program (2009CB723905,
2011CB707106), the NSFC (10978003, 41127901).
