ARS  Vol.2 No.1 , March 2013
Noise Reduction in White Light Lidar Signal Using a One-Dim and Two-Dim Daubechies Wavelet Shrinkage Method
Abstract: A 1-D and 2-D Daubechies 5 (db5) discrete wavelet shrinkage methods using a 10 level decomposition was applied to white light lidar data particularly at 350 nm and 550 nm backscattered signal. At 350 nm, the backscattered signal is very weak as compared to 550 nm backscattered signal because of the spectral intensity distribution of the generated white light. The 1-D and 2-D wavelet shrinkage method gave a much better result as compared with the moving average method. However, the 2-D wavelet shrinkage method produced a much better denoised lidar signal compared with the 1-D wavelet shrinkage method. This is indicated by the 142% increase in correlation coefficient between the 2-D denoised lidar signal and the 800 nm original lidar signal as compared with only 12% increase in correlation coefficient for the 1-D denoised lidar signal. The 2-D wavelet shrinkage method also gave a much higher SNR value of 65.9 compared to 1-D which is 38.8.
Cite this paper: T. Somekawa, M. Galvez, M. Fujita, E. Vallar and C. Yamanaka, "Noise Reduction in White Light Lidar Signal Using a One-Dim and Two-Dim Daubechies Wavelet Shrinkage Method," Advances in Remote Sensing, Vol. 2 No. 1, 2013, pp. 10-15. doi: 10.4236/ars.2013.21002.

[1]   M. C. D. Galvez, et al., “Three-Wavelength Backscatter Measurement of Clouds and Aerosols Using a White Light Lidar System,” Japanese Journal of Applied Physics, Vol. 41, No. 3, 2002, pp. L284-L286. doi:10.1143/JJAP.41.L284

[2]   P. Rairoux, et al., “Remote Sensing of the Atmosphere Using Ultrashort Laser Pulses,” Applied Physics B, Vol. 71, No. 4, 2000, pp. 573-580. doi:10.1007/s003400000375

[3]   T. Somekawa, M. Fujita, C. Yamanaka and M. C. D. Galvez, “Depolarization Light Detection and Ranging Using a White Light LIDAR System,” Japanese Journal of Applied Physics, Vol. 45, No. 6, 2006, pp. L165-L168. doi:10.1143/JJAP.45.L165

[4]   H. T. Fang and D. S. Huang, “Noise Reduction in Lidar Signal Based on Discrete Wavelet Transform,” Optics Communications, Vol. 233, No. 1-3, 2004, pp. 67-76. doi:10.1016/j.optcom.2004.01.017

[5]   M. C. D. Galvez, T. Somekawa, C. Yamanaka and M. Fujita, “Wavelet Denoising Applied to Multiwavelength Depolarization White Light Lidar Measurement,” Proceedings of the 23rd International Laser Radar Conference, Nara, 24-28 July 2006, pp. 275-278.

[6]   I. Daubechies, “Ten Lectures on Wavelets,” Society for Industrial and Applied Mathematics, Philadelphia, 1992. doi:10.1137/1.9781611970104

[7]   D. L. Donoho, “De-Noising by Soft-Thresholding,” IEEE Transactions on Information Theory, Vol. 41, No. 3, 1995, pp. 613-627. doi:10.1109/18.382009

[8]   S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, 1989, pp. 674-693. doi:10.1109/34.192463

[9]   B. Walczak and L. Massart, “Noise Suppression and Signal Compression Using the Wavelet Packet Transform,” Chemometrics and Intelligent Laboratory Systems, Vol. 36, No. 2, 1998, pp. 81-94. doi:10.1016/S0169-7439(96)00077-9