Abstract
Remote sensing technique of lidar belongs to the category of weak signal extraction under strong background noise. For effectively reducing the noise of lidar return signal, a wavelet analysis method using local threshold value is employed. In the local threshold value wavelet method, different threshold values are used to quantify the high frequency coefficients of every decomposition layer. Both the numerical simulation signal contaminated by random noise of different standard deviation and the practical Mie lidar returns were adopted, and the comparisons among sliding-window method, global threshold method and local threshold method were performed for verifying the feasibility of the local threshold method. Experiment results show that the local threshold wavelet method is a useful de-noising method which shows better effects of noise reduction than other two methods.
Similar content being viewed by others
References
Ehara N., Sasase I., Mori S.: Weak radar signal detection based on wavelet transform. Trans. IEICE 77-B-II(5), 259–267 (1994)
Fang H., Huang D., Wu Y.: Antinoise approximation of the lidar signal with wavelet neural networks. Appl. Opt. 44(6), 1077–1083 (2005)
Fang H., Huang D.: Noise reduction in lidar signal based on discrete wavelet transform. Opt. Commun. 233(1–3), 67–76 (2004)
Han Y., Westwater E.R., Ferrare R.A.: Applications of Kalman filtering to derive water vapor profiles from Raman lidar and microwave radiometers. J. Atmos. Ocean. Technol. 14(3), 480–487 (1997)
Hu, C., Li, G., Liu, T. et al.: Wavelet Analysis of System Analysis and Design based on Matlab6. X[M], vol. 1. Xidian University Press, Xi’an (2004)
Huang D., Wu Y.: Antinoise approximation of the lidar signal with wavelet neural networks. Appl. Opt. 44(6), 1077–1083 (2005)
Klett J.D.: Stable analytical inversion solution for processing lidar returns. Appl. Opt. 20, 211–220 (1981)
Matz V., Kreidl M., Šmíd R.: Signal-to-noise ratio improvement based on the discrete wavelet transform in ultrasonic defectoscopy. Acta Polytechnica 44(4), 61–66 (2004)
Mao J., Hua D., He T., Wang M.: Lidar observations of atmospheric aerosol optical properties over Yinchuan area. Spectrosc. Spectr. Anal. 30(7), 2006–2010 (2010)
Sasano Y.: Tropospheric aerosol extinction scanning lidar measurements over Tsukuba, Japan, from coefficient profiles derived from 1990 to 1993. Appl. Opt. 35(24), 4941–4952 (1996)
Sun Y.: Wavelet Analysis and Its Application, vol. 3. Mechanical Industry Press, Beijing (2005)
Volkov S.N., Kaul B.V., Shelefontuk D.I.: Optimal method of linear regression in laser remote sensing. Appl. Opt. 41(24), 5078–5083 (2002)
Wu S., Liu Z., Liu B.: Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method. Opt. Commun. 267(1), 137–144 (2006)
Wu, Y.: The study of the method based on wavelet in signal de-noising. vol. 5. Dissertation of Master Degree of Wuhan University of Technology, Wuhan (2007)
Xiang , Xiang : Extracting characteristic information of weak signal from strong noise background by wavelet analysis. Dissertation of Master Degree of Wuhan University, Wuhan. 267(1), 137–144 (2006)
Yin S., Wang W.: Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering. Chin. Opt. Lett. 4(12), 694–696 (2006)
Zheng F., Hua D., Zhou A.: Empirical mode decomposition algorithm research & application of Mie lidar atmospheric backscattering signal. Chin. J. Lasers 36(5), 1068–1074 (2009)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mao, J. Noise reduction for lidar returns using local threshold wavelet analysis. Opt Quant Electron 43, 59–68 (2012). https://doi.org/10.1007/s11082-011-9503-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11082-011-9503-6