Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210200160-5.doi: 10.11896/jsjkx.210200160

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Improved Water Quality Remote Sensing Monitoring Algorithms Based on Multilayer Convolutional Neural Network

FENG Lei1,3, FENG Li2,4, FANG Fang1, GUO Jin-song1, PAN Jiang5, YU You1, CHEN Yu6   

  1. 1 College of Environment and Ecology,Chongqing University,Chongqing 400044,China
    2 Chongqing Academy of Ecological and Environmental Sciences(Southwest Branch of China Academy of Environmental Sciences),Chongqing 401147,China
    3 Super Resolution Optics Research Center,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China
    4 College of Materials Science and Engineering,Chongqing University,Chongqing 400044,China
    5 College of Chemistry and Chemical Engineering,Chongqing University of Technology,Chongqing 400054,China
    6 Beijing Productivity Promotion Center,Beijing 100088,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:FENG Lei,born in 1990,Ph.D.His main research interests include environmental information and environmental remote sensing research.
    CHEN Yu,born in 1990,assistant researcher.Her main research interests include deep learning modeling and so on.
  • Supported by:
    Chongqing Science and Technology Projects(HuanKeZi 2018 No.04,cstc2018jszx zdyfxmx0020).

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Abstract: 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 hot-spot 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.

Key words: Neural network, Three Gorges reservoir, Eutrophication, Deep learning, Remote sensing monitoring

CLC Number: 

  • TP181
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