A Multi-Source Convolutional Neural Network for Lidar Bathymetry Data Classification

Abstract

Airborne Lidar bathymetry (ALB) has been widely applied in coastal hydrological research due to outstanding advantages in integrated sea-land mapping. This study aims to investigate the classification capability of convolutional neural networks (CNN) for land echoes, shallow water echoes and deep water echoes in multichannel ALB systems. First, the raw data and the response function after deconvolution were input into the network via different channels. The proposed multi-source CNN (MS-CNN) was designed with a one-dimensional (1 D) squeeze-and-excitation module (SEM) and a calibrated reference module (CRM). The classification results were then output by the SoftMax layer. Finally, the accuracy of MS-CNN was validated on the test sets of land, shallow water and deep water. The results show that more than 99.5% have been correctly classified. Besides, it has suggested the best robustness of the proposed MS-CNN compared with other advanced classification algorithms. The results indicate that CNN is a promising candidate for the classification of Lidar bathymetry data.

Keywords:

• Airborne Lidar bathymetry
• convolutional neural network
• full-waveform classification
• light detection and ranging

Disclosure statement

No potential competing interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Additional information

Funding

This work is supported by the Guangxi Innovative Development Grand Grant (No. 2018AA13005) and the Science and Technology Project of Tianjin (No. 18ZXZNGX00230).