Algorithm SNR RMSE 
Combination method 21.94 0.0065 
Direct average method 17.54 0.0110 
Forced de-noising method 19.00 0.0093 
FFT method 21.34 0.0070 
  
Table 1. SNR and RMSE of different de-noising algorithms 
In the Table 1, the SNR of combination method is highest and 
the RMSE is smallest. So, the combination of modulus maxima 
method and threshold method based on wavelet transform can 
effectively eliminate different noise of backscatter signals and 
make the extracted signal cognizable. 
5. CONCLUSIONS 
In this paper, a new lidar with red and near infrared wavelengths 
was proposed to apply for features monitoring of different 
objects. However, backscatter signals of the lidar system are 
seriously strongly influenced by variety of noise and signal 
noise which has to be reduced. Based on the wavelet transform, 
combination method of modulus maxima method and threshold 
method was proposed. The results tested the ability of the 
algorithm after comparison of other classical algorithms. In fact, 
the de-noised signals had smooth waveforms and the SNR was 
also improved significantly after the de-noising processing. 
Nevertheless, there are still some questions over this method, 
and the algorithm should be further improved in the subsequent 
work. 
REFERENCES 
A. Wehr and U. Lohr, 1999. Airborne laser scanning—An 
introduction and overview. ISPRS Journal of Photogrammetry 
and Remote Sensing, 54(2/3), pp. 68-82. 
HE Shi-biao, YANG Shi-zhong, 2002. Applied of 3o-rule in 
Reducing Noise in Signal by Wavelet analysis. Journal of 
Chongqing University(Natural Science Edition), 25(12), pp.58- 
61. 
J Grace, C Nichol, et al, 2007. Can we measure terrestrial 
photosynthesis from space directly, using spectral reflectance 
and fluorescence. Global Change Biology, 13(7), pp.1484-1497. 
K. Kraus and N. Pfeifer, 1998. Determination of terrain models 
in wooded areas with airborne laser scanner data. /SPRS 
Journal of Photogrammetry and Remote Sensing, 53(4), pp. 
193-203. 
Liang Zhang , Demin Wang ,et al, 2002 . Adaptive SPIHT for 
image coding based on cured wavelet transform. The 
International Society for Optical Engineering, 5685, pp. 160- 
163. 
MALLAT S. Singularity detection and processing with wavelets. 
IEEE Transactions on Information Theory, 1992. 38(2), pp.617 
- 643. 
Zhengyou He, Yumei Cai, et al, 2004. A study of wavelet 
entropy theory and its application in power system. [EEE 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
International Conference on Intelligent Mechatronics and 
Automation, 8, pp.847-851. 
ACKNOWLEDGEMENTS 
This work was supported by 973 Program (2009CB723905), the 
NSFC (41101334, 10978003, 41127901), the Fundamental 
Research Funds for the Central Universities.