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New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm.

Zhenzhu Wang1,2,3, Hongbo Ding1,2, Bangxin Wang1,2,3

  • 1Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

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Summary

A new denoising method, the Wavelet Transform-Variational Mode Decomposition (WT-VMD) joint algorithm optimized by the Sparrow Search Algorithm (SSA), effectively reduces noise in Light Detection and Ranging (LIDAR) signals. This advanced technique significantly improves signal-to-noise ratio and reduces root-mean-square error for enhanced data accuracy.

Keywords:
EMDSSAVMDWTWT-VMDdenoisinglidar

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Analysis

Background:

  • Light Detection and Ranging (LIDAR) is a crucial active remote sensing technology.
  • LIDAR echo signals are inherently non-linear, non-stationary, and susceptible to noise.
  • Effective noise reduction is vital for accurate signal information extraction.

Purpose of the Study:

  • To introduce and evaluate a novel denoising method for LIDAR signals.
  • To compare the performance of the proposed method against existing techniques.
  • To demonstrate the effectiveness of the new algorithm in improving LIDAR data quality.

Main Methods:

  • Comparative experimental analysis of common denoising methods including Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD).
  • Development and application of a novel WT-VMD joint algorithm incorporating the Sparrow Search Algorithm (SSA) for LIDAR signal denoising.
  • Validation using simulated LIDAR signals with varying pulse counts (50, 100, 1000) and simulated noise (with and without aerosol and clouds).

Main Results:

  • The WT-VMD joint algorithm based on SSA demonstrated superior performance compared to other methods.
  • Significant improvements in signal-to-noise ratio (SNR) and reductions in root-mean-square error (RMSE) were observed across all simulated signal types.
  • For unpolluted signals, SNR increased by up to 138.5% and RMSE decreased by up to 81.8%.
  • For signals with aerosol and clouds, SNR increased by up to 83.3% and RMSE decreased by up to 70.8%.

Conclusions:

  • The WT-VMD joint algorithm optimized by SSA is the most suitable method for LIDAR signal denoising.
  • This method achieves the maximum SNR and minimum RMSE, indicating excellent noise reduction capabilities.
  • Application to actual LIDAR data confirmed its extraordinary denoising effect, promising improved inversion accuracy.