A deep learning method for LiDAR bathymetry waveforms processing

Yaxin Liu; Jun Yue; Peng Shi; Yuchen Wang; Hongxiu Gao; Baocheng Feng; Zunnian Liu; Hongsheng Li

doi:10.1117/12.2615112

15 October 2021 A deep learning method for LiDAR bathymetry waveforms processing

Yaxin Liu, Jun Yue, Peng Shi, Yuchen Wang, Hongxiu Gao, Baocheng Feng, Zunnian Liu, Hongsheng Li

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119331S (2021) https://doi.org/10.1117/12.2615112
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

The shallow water areas, such as near shore, near reef, shallow sea and wetland, are blind areas for detection, and are also important research fields of LiDAR bathymetry technology. In this paper, we used the self-developed LiDAR bathymetry experimental system to obtain echo waveforms in the air and water tank in the laboratory. A deep learning method of one-dimensional convolutional neural network was proposed to directly invert the depth of water based on these original experimental echo data. The results denote that the deep learning method is feasible for the processing of large amount of LiDAR bathymetry echo waveforms. This method can be further used for echo waveforms processing outdoor.

Citation Download Citation

Yaxin Liu, Jun Yue, Peng Shi, Yuchen Wang, Hongxiu Gao, Baocheng Feng, Zunnian Liu, and Hongsheng Li "A deep learning method for LiDAR bathymetry waveforms processing", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119331S (15 October 2021); https://doi.org/10.1117/12.2615112

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