基于海洋激光雷达和BP神经网络的叶绿素剖面反演算法  

Chlorophyll Profile Retrieval Algorithm Based on Oceanographic Lidar and BP Neural Network

作　　者：铁凝 刘秉义[1] Tie Ning;Liu Bingyi(College of Marine Technology,Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,Shandong,China)

机构地区：[1]中国海洋大学信息科学与工程学部海洋技术学院,山东青岛266100

出　　处：《光学学报》2023年第24期67-78,共12页Acta Optica Sinica

基　　金：国家重点研发计划(2022YFB3901702,2016YFC1400905)。

摘　　要：激光在海水传输过程中,接收视场逐渐增大、多次散射逐渐增强,导致叶绿素剖面反演误差较大。针对该问题,以反向传播(BP)神经网络为基础,建立了一个激光雷达回波信号反演叶绿素a浓度剖面的模型。数据集中包含标签-叶绿素a浓度与特征-激光雷达回波信号剖面。获得激光雷达回波剖面后,为了增强输入特征,使用随机抽样一致(RANSAC)算法剔除噪声,完成数据集搭建,进而以不同层网络的均方误差确定模型结构。反演结果显示:相比于传统的利用后向散射系数反演叶绿素浓度的方法,基于激光雷达回波的BP神经网络叶绿素剖面反演算法在验证集上的相对误差、均方根误差、平均误差分别降低了34%、0.363 mg/m^(3)和0.213 mg/m^(3),相关系数提高了0.18。传统方法在50 m深度水体的叶绿素浓度相对误差为39%~93%,基于神经网络的剖面反演算法对应的相对误差为17%~36%,反演精度具有较大提升。对于实测数据,LIMC-BPNN反演结果的相对误差为13%。结果表明,相比于传统叶绿素反演方法,基于深度学习的叶绿素剖面反演算法能够有效提取激光雷达回波特征,得到更好的反演结果。Objective Currents,sea waves,and climate changes are generated by air-sea interaction.Oceans cover more than 70 percent of the earth and play a significant role in the ecological environment.Therefore,various countries are researching oceans.A wide range of substances are present in oceans,of which phytoplankton are important primary producers in the marine ecosystem and are linked to a variety of oceanic processes.Chlorophyll a is an indicator to characterize the phytoplankton amount and plays an indispensable part in ocean research.Meanwhile,bio-optical parameters can be employed in various fields of oceanographic research and contribute to the rapid development of marine research.Chlorophyll concentration is an important prerequisite for the inversion of bio-optical parameters,and it directly affects the results of the bio-optical parameters.Active remote sensors with outstanding advantages have become one of the most rapidly growing and most effective remote sensors in recent years.Since active remote sensing technology does not depend on solar rays,it can obtain profile information with few detection limitations.As active remote sensing,oceanographic lidar can be mounted on a variety of platforms and obtain the profile concentration of chlorophyll.However,the traditional methods of inverting chlorophyll from lidar signals have poor accuracy, because they are susceptible to multiple scattering.Therefore, high-precision chlorophyll inversion algorithms are essential for marine research. Since the echo signal and chlorophyll concentration have a complex nonlinear relationship, deep learning can be adopted to filter multiple scattering noises, extract the backward scattering signal features, and build a high-precision chlorophyll inversion model.Methods Four steps are conducted as follows. Firstly, a dataset is built with two parts of label and feature. The label consisting of chlorophyll concentration profiles comes from BGC-Argo and the chlorophyll optical parameters are calculated by empirical relations. The

关 键 词：海洋光学 反向传播神经网络 垂直剖面 水体光学参数 叶绿素a 

分 类 号：P714.1[天文地球—海洋科学]