摘要
随着水环境质量监测技术的高速发展,水环境质量数据的种类、数量均都呈现爆炸式增长。原位监测与遥感监测是水环境监测的重要数据来源,如何快速高效地理解海量的监测数据是人工智能技术在生态环境研究领域的热点。因此,以三峡库区境内的国家良好水体——长寿湖为例,研究改进WRCNN卷积神经网络算法模型直接对遥感影像中的水环境数据进行特征提取,结合原位在线监测数据对遥感影像数据进行标注,增加CNN网络的宽度,提高遥感数据的水环境特征提取的能力,消除函数选择的不确定性,减少参数确定带来的计算步骤和抑制过拟合的影响,实现对大尺度水环境遥感特征的利用。结果表明,改进WRCNN卷积神经网络算法模型能有效识别长寿湖富营养化表征指标叶绿素a的浓度,为库区水体富营养化监测提供高效手段。
With the rapid development of water environment online monitoring technology in recent years,the categories and quantities of monitoring data have been greatly improved.Online monitoring and remote sensing monitoring are important data sources for water environment monitoring.How to quickly and efficiently understand massive monitoring data is a research hotspot of artificial intelligence technology in the field of ecological environment data research.Changshou lake is a national good water body in the Three Gorges reservoir area.This paper aims at proposing an improved CNN convolution neural network algorithm WRCNN model,and this model is studied to extract features directly from water environment data in remote sensing images and increasing data dimension of water monitoring data.The ability of extraction can eliminate the uncertainty of function selection,reduce the calculation steps,suppress the influence of over-fitting and realize the application of remote sensing technology of large-scale monitoring in water environment.The results show that the improved WRCNN convolution neural network algorithm model can effectively identify the concentration of chlorophyll,the indicator of eutrophication in Changshou lake,and provide an efficient measures for monitoring eutrophication in reservoir area.
作者
封雷
封丽
方芳
郭劲松
潘江
余由
陈瑜
FENG Lei;FENG Li;FANG Fang;GUO Jin-song;PAN Jiang;YU You;CHEN Yu(College of Environment and Ecology,Chongqing University,Chongqing 400044,China;Chongqing Academy of Ecological and Environmental Sciences(Southwest Branch of China Academy of Environmental Sciences),Chongqing 401147,China;Super Resolution Optics Research Center,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China;College of Materials Science and Engineering,Chongqing University,Chongqing 400044,China;College of Chemistry and Chemical Engineering,Chongqing University of Technology,Chongqing 400054,China;Beijing Productivity Promotion Center,Beijing 100088,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期388-392,共5页
Computer Science
基金
重庆市科技攻关项目(应用技术研发类)(环科字2018第04号,cstc2018jszx-zdyfxmX0020)
关键词
神经网络
三峡库区
富营养化
深度学习
遥感监测
Neural network
Three Gorges reservoir
Eutrophication
Deep learning
Remote sensing monitoring
