Skip to main content
Log in

Noise reduction for lidar returns using local threshold wavelet analysis

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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)

    Google Scholar 

  • Fang H., Huang D., Wu Y.: Antinoise approximation of the lidar signal with wavelet neural networks. Appl. Opt. 44(6), 1077–1083 (2005)

    Article  ADS  Google Scholar 

  • Fang H., Huang D.: Noise reduction in lidar signal based on discrete wavelet transform. Opt. Commun. 233(1–3), 67–76 (2004)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Klett J.D.: Stable analytical inversion solution for processing lidar returns. Appl. Opt. 20, 211–220 (1981)

    Article  ADS  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • Sun Y.: Wavelet Analysis and Its Application, vol. 3. Mechanical Industry Press, Beijing (2005)

    Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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)

    Article  ADS  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    ADS  Google Scholar 

  • 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiandong Mao.

Rights and permissions

Reprints 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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11082-011-9503-6

Keywords

Navigation