A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network

Minghuan Hu,Jiandong Mao,Juan Li,Qiang Wang,Yi Zhang

doi:10.3390/atmos12111403

Minghuan Hu, Jiandong Mao + Show 3 more

Open Access

https://doi.org/10.3390/atmos12111403

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Abstract

The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.

Highlights

Jorge et al carried out a preliminary study on the applicability of deep learning to improve biomass estimation based on lidar, and the results showed that the autoencoder statistically improved the quality of multi linear regression estimation [14]
In order to evaluate the effect of denoising algorithm, both the signal-to-noise ratio (SNR) and mean-square error (MSE) are generally adopted for evaluation [25]
The algorithm proposed in this paper has strong adaptive ability, and its excellent denoising effect can be seen through the calculation of SNR and MSE

Summary

Introduction

As an active measurement method, lidar has been widely used in atmospheric remote sensing and environmental monitoring due to its high temporal and spatial resolution. It has made important progress in the fine detection of atmospheric aerosol optical properties, microphysical properties, atmospheric temperature, relative humidity and other parameters, and has become an important tool for the study of atmospheric environmental parameters and their spatial-temporal evolution. In the actual detection process, the lidar return signal is greatly affected by noise. It is of great importance to reduce the noise of the return signal

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Results

Conclusion