基于高分遥感影像的总磷浓度反演研究  

Total Phosphorus Concentration Inversion Research Based on High-resolution Remote Sensing Images

作　　者：吴欢欢 春兰 王春晓 国巧真 刘晓娟 熊小青 WU Huanhuan;CHUN Lan;WANG Chunxiao;GUO Qiaozhen;LIU Xiaojuan;XIONG Xiaoqing(Hainan Geometics Center,Ministry of Natural Resources,Haikou 570100,China;School of Geology and Geomatics,Tianjin Chengjian University,Tianjin 300384,China)

机构地区：[1]自然资源部海南基础地理信息中心,海南海口570100 [2]天津城建大学地质与测绘学院,天津300384

出　　处：《测绘与空间地理信息》2024年第8期18-21,共4页Geomatics & Spatial Information Technology

基　　金：自然资源部海洋测绘重点实验室开放研究基金——基于海洋遥感大数据的海岸带和近岸海域遥感监测关键技术与应用研究(2021A02);自然资源部科技创新人才培养工程青年人才资助项目——自然资源监测关键技术与应用研究(12110600000018003932)资助。

摘　　要：以定量遥感反演水质参数为目的,以天津市海河下游段为研究区,利用总磷实测水质数据和同期GF-2PMS 2遥感影像数据,建立两者的偏最小二乘回归模型、单隐含层神经网络模型、双隐含层神经网络模型及粒子群算法优化的双隐含层神经网络模型(DP-BPNN模型)。通过决定系数、平均绝对误差、均方根误差进行精度检验,选出研究区水体适用的总磷浓度的反演模型。结果表明:与偏最小二乘模型精度对比,所建总磷反演模型精度提高了48%。In this paper,the inversion model for total phosphorus concentration was established based on the measured water quality data and GF-2 satellite data in the Haihe River Basin,Tianjin.The optimal inversion model for total phosphorus concentration was determined by comparing the partial least squares regression model,the single hidden layer neural network model,the double hidden layer neural network model and the dual neural network model optimized by the particle swarm algorithm.Through the determination coefficient,mean absolute error,and root mean square error,the accuracy was verified,and the inversion model of total phosphorus concentration applicable to the water body in the study area was selected.The results show that the accuracy of DP-BPNN of total phosphorus concentrations is improved by 48%as compared with that of the partial least squares model.

关 键 词：神经网络模型 粒子群优化算法 总磷 GF-2遥感影像 海河 

分 类 号：P237[天文地球—摄影测量与遥感]