大气与环境光学学报2025，Vol.20Issue(3)：410-422,13.DOI:10.3969/j.issn.1673-6141.2025.03.013
基于双向长短期记忆网络的机载海洋激光雷达海表位置提取方法
A sea surface position extraction method for airborne marine lidar based on bidirectional long short-term memory network
摘要
Abstract
Accurate sea surface positioning and extraction are critical factors influencing the measurement precision and bathymetric accuracy of airborne oceanographic lidar.When 532 nm lidar is used for sea surveying and mapping,the scattering and absorption of sea surface waves and near-surface water after laser penetrating through the air-sea interface will degrade the accuracy of sea surface positioning at this wavelength.This study employed a Bidirectional Long Short-Term Memory(Bi-LSTM)network for sea surface positioning.According to Bi-LSTM method,the complete sea surface return waveforms from dual-wavelength detection(1064 nm and 532 nm)were used for training,and then the 1064 nm detection results were used as ground truth to determine the sea surface positions of 532 nm channel.Application of the trained model to 532 nm detection data showed that the average positioning bias using Bi-LSTM algorithm was 0.03 m,while that of conventional peak detection algorithm was-0.21 m.Bi-LSTM algorithm was further used to quantitatively evaluate the influence of seawater diffuse attenuation coefficient on the bias,and the results showed that the minimum bias of 0.03 m occurred when the seawater diffuse attenuation coefficient was in the range of 0.05-0.15 m-1.关键词
机载激光测深/海洋激光雷达/海表位置/深度学习/海水光学性质Key words
airborne laser bathymetry/marine lidar/sea surface position/deep learning/optical properties of seawater分类
海洋学引用本文复制引用
汤晨,姜萍,展文军,郭子钰,丁缪琦,宋小全..基于双向长短期记忆网络的机载海洋激光雷达海表位置提取方法[J].大气与环境光学学报,2025,20(3):410-422,13.基金项目
国家重点研发计划资助(2022YFB3901705,2022YFC3700402) （2022YFB3901705,2022YFC3700402）
