基于无人机影像与机器学习的钙华湖泊水深反演  

Water depth retrieval in tufa lakes based on Unmanned Aerial Vehicle(UAV)imagery and machine learning

作　　者：何金宸 张书航 冯伟[1,3] 燕兴元[1,3] 晋泽辉 林家元 HE Jinchen;ZHANG Shuhang;FENG Wei;YAN Xingyuan;JIN Zehui;LIN Jiayuan(School of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai 519082,China;Key Laboratory of Marine Environmental Survey Technology and Application,Ministry of Natural Resources,Guangzhou 510300,China;Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University),Ministry of Education,Zhuhai 519082,China;Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station,School of Geographical Sciences,Southwest University,Chongqing 400715,China)

机构地区：[1]中山大学测绘科学与技术学院,珠海519082 [2]自然资源部海洋环境探测技术与应用重点实验室,广州510300 [3]极地环境立体观测与应用教育部重点实验室(中山大学),珠海519082 [4]西南大学重庆金佛山喀斯特生态系统国家野外科学观测研究站,重庆400715

出　　处：《遥感学报》2025年第1期181-190,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：自然资源部海洋环境探测技术与应用重点实验室开放基金(编号:MESTA-2022-B004);湖北省自然科学基金计划杰出青年人才项目(编号:2019CFA091);中山大学中央高校基本科研业务费专项(编号:22lgqb09,23qnpy09)。

摘　　要：高分辨率且非接触式的水深监测对钙华湖泊景观的管理与保护至关重要。卫星遥感测深无法捕获钙华湖泊细微的的水下沉积特征。近年来,轻小型无人机遥感技术逐渐应用于浅水区超高分辨率的水深探测。然而,水深反演中经典的对数模型难以适应钙华湖泊内广泛存在的瑞利散射现象。因此,本文利用机器学习模型开展基于无人机影像的钙华湖泊水深反演研究。以中国四川省九寨沟火花海为实验区,对基于随机森林RF(Random Forest)、支持向量机SVM(Support Vector Machine)与多层感知机MLP(Multi-Layer Perceptron)的水深反演模型进行训练与验证,其均方根误差依次为0.816 m、0.945 m、0.832 m。实验结果表明,机器学习模型相较于传统的对数模型具有更高的水深反演精度。其中,随机森林模型与多层感知机模型比支持向量机模型更适合基于无人机影像的钙华湖泊水深反演。High-resolution and noncontact water depth monitoring is crucial for the management and protection of tufa lake landscapes.Satellite-derived bathymetry cannot capture the subtle underwater sedimentary characteristics of tufa lakes.In recent years,the remote sensing technology of light and small Unmanned Aerial Vehicles(UAVs)has gradually been applied to ultrahigh-resolution bathymetric mapping in shallow water areas.However,the classic logarithmic model in water depth inversion is difficult to adapt to the widespread Rayleigh scattering phenomenon in tufa lakes.Therefore,in this study,machine learning methods are used to construct bathymetric inversion models of tufa lakes based on UAV imagery.Taking Spark Lake in Jiuzhaigou National Nature Reserve,Sichuan Province,China as the experimental area,this study extracts aerial image data for bathymetric model construction from UAV platforms.On the basis of pre-and post-earthquake UAV images,a preearthquake orthophoto with water and a post-earthquake surface model without water are generated using the structure-from-motion algorithm.After the exclusion of anomalous areas,sample points for the bathymetric inversion are randomly selected.Each sample datum includes the red,green,and blue band digital number values of the pre-earthquake orthophoto and the relative depth values of the postearthquake exposed terrain relative to the pre-earthquake water surface.Through this dataset,machine learning regression models based on Random Forest(RF),Support Vector Machine(SVM),and Multilayer Perceptron(MLP)are constructed.The above machine learning models are trained repeatedly to determine their respective optimal parameters.The accuracy of the estimated bathymetry is verified using the exposed lake terrain after the earthquake.Results indicate that the water depth distribution of the three models has small differences in shallow water areas,and the areas with significant differences are mainly deep-water regions.The bathymetric map simulated by the RF model is susceptible to high-

关 键 词：钙华湖泊 无人机 航空影像 水深反演 机器学习 随机森林 支持向量机 多层感知机 

分 类 号：P237[天文地球—摄影测量与遥感] P2[天文地球—测绘科学与技术]