2022/01/22 by Yiqiang Zhao, Xuemin Yu, Bin Hu +1 · 9 citations
Earth and Planetary Sciences · Environmental Science · #Algorithm #Artificial intelligence #Bathymetry #Cartography #Computer science #Convolutional neural network #Deconvolution #Geography #Lidar #Ocean Waves and Remote Sensing #Pattern recognition (psychology) #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Remote sensing #Robustness (evolution) #Softmax function
paper · doi:10.1080/01490419.2022.2032498
published in Marine Geodesy 45(3), 232-250 (Taylor & Francis)
openalex publication_date 2022/01/22 · crossref created 2022/01/22 · crossref issued 2022/02/04 · crossref published 2022/02/04 · crossref published-online 2022/02/04 · crossref deposited 2022/04/21 · crossref published-print 2022/05/04 · openalex created_date 2025/10/10 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/30
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.