Journal
ATMOSPHERE
Volume 12, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/atmos12111403
Keywords
lidar; deep learning; autoencoder; convolutional neural network; denoising
Funding
- National Natural Science Foundation of China [61765001, 61565001]
- Natural Science Foundation of Ningxia Province [2021AAC02021]
- Plan for Leading Talents of the State Ethnic Affairs Commission of the People's Republic of China
- Innovation Team of Lidar Atmosphere Remote Sensing of Ningxia Province
- high level talent selection and training plan of North Minzu University
- Research Project of Serving Nine Key Industrial Projects for Ningxia of North Minzu University [FWNX20]
- Ningxia First-Class Discipline and Scientific Research Projects (Electronic Science and Technology) [NXYLXK2017A07]
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A novel denoising method based on a convolutional autoencoding neural network was proposed to reduce noise in lidar signals, achieving better results compared to other methods when tested on simulated and measured signals.
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.
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