基于改进多层卷积神经网络的水体富营养化遥感监测算法研究

doi:10.11896/jsjkx.210200160

计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210200160-5.doi: 10.11896/jsjkx.210200160

• 图像处理&多媒体技术 • 上一篇下一篇

基于改进多层卷积神经网络的水体富营养化遥感监测算法研究

封雷^1,3, 封丽^2,4, 方芳¹, 郭劲松¹, 潘江⁵, 余由¹, 陈瑜⁶

1 重庆大学生态与环境学院重庆 400044
2 重庆市生态环境科学研究院(中国环境科学研究院西南分院) 重庆 401147
3 中国科学院重庆绿色智能技术研究院超分辨光学研究中心重庆 400714
4 重庆大学材料学院重庆 400044
5 重庆理工大学化学化工学院重庆 400054
6 北京生产力促进中心北京 100088

出版日期:2022-11-10 发布日期:2022-11-21
通讯作者: 陈瑜(chenyu_310@126.com)
作者简介:(fenglei@cigit.ac.cn)
基金资助:
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)

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

FENG Lei^1,3, FENG Li^2,4, FANG Fang¹, GUO Jin-song¹, PAN Jiang⁵, YU You¹, CHEN Yu⁶

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).

摘要/Abstract

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。

关键词: 神经网络, 三峡库区, 富营养化, 深度学习, 遥感监测

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

中图分类号:

TP181

封雷, 封丽, 方芳, 郭劲松, 潘江, 余由, 陈瑜. 基于改进多层卷积神经网络的水体富营养化遥感监测算法研究[J]. 计算机科学, 2022, 49(11A): 210200160-5. https://doi.org/10.11896/jsjkx.210200160

FENG Lei, FENG Li, FANG Fang, GUO Jin-song, PAN Jiang, YU You, CHEN Yu. Improved Water Quality Remote Sensing Monitoring Algorithms Based on Multilayer Convolutional Neural Network[J]. Computer Science, 2022, 49(11A): 210200160-5. https://doi.org/10.11896/jsjkx.210200160

参考文献

[1]ZOU J Y.Research on water quality monitoring system based on ZigBee and Labview technology [D].Hangzhou:China University of Metrology,2016.
[2]GRIFFITH J F,WEISBERG S B,SOBIN J M.Challenges in Implementing New Technology for Beach Water Quality Monitoring:Lessons From a California Demonstration Project[J].Marine Technology Society Journal,2011,45(2):65-73.
[3]CUI Y,ZHU G W,ZHANG Y L,et al.Research on high-frequency monitoring method for lake and reservoir eutrophication indicators Research [J].Journal of Environmental Science,2014,34(5):1178-1185.
[4]TRESCOTT A,PARK M H.Remote sensing models usingLandsat satellite data to monitor algal blooms in Lake Champlain[J].Water Science & Technology,2013,67(5):1113-1120.
[5]WU H,YE L P,SHI W Z,et al.Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan,China[J].International Journal of Applied Earth Observation and Geoinformation,2014,32:67-68.
[6]LI Y P,HUA L,TAN Y F,et al Simulation of water transparency in shallow lakes based on bio optical model [J].Journal of Hydropower,2013,32(6):8.
[7]PAN Z,HU Y,CAO B.Construction of smooth daily remotesensing time series data:a higher spatiotemporal resolution pers-pective[J].Open Geospatial Data,Software and Standards,2017,2(1):25.
[8]HE L Y,YING G G,LIU Y S,et al.Discharge of swine wastes risks water quality and food safety:Antibiotics and antibiotic resistance genes from swine sources to the receiving environments[J].Environment International,2016,92/93:210-219.
[9]BOWES M J,LOEWENTHAL M,READ D S,et al.Identifying multiple stressor controls on phytoplankton dynamics in the River Thames(UK) using high-frequency water quality data[J].Science of the Total Environment,2016:569/570:1489-1499.
[10]KAI Z,ZUO W,GU S,et al.Learning Deep CNN Denoiser Prior for Image Restoration[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2017:2808-2817.
[11]JOURABLOO A,LIU X.Pose-Invariant Face Alignment viaCNN-Based Dense 3D Model Fitting[J].International Journal of Computer Vision,2017,124(2):187-203.
[12]BLOM H A.Indirect measurement of key water quality parameters in sewage treatment plants[J].Journal of Chemometrics,1996,10(5/6):697-706.
[13]YUN R,CHANGREN Z,SHUNPING X.Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN[J].Applied Sciences,2018,8(5):813.
[14]SHEN T ,ZHOU T ,LONG G ,et al.DiSAN:Directional Self-Attention Network for RNN/CNN-free Language Understan-ding[C]//National Conference on Artificial Intelligence.Association for the Advancement of Artificial Intelligence(AAAI).2018:2377-2380.
[15]CAO R,ZHANG Q,ZHU J,et al.Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network[J].International Journal of Remote Sensing,2020,41(2):740-751.
[16]CHAIB S,YAO H,GU Y,et al.Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models[C]//Ninth International Conference on Digital Image Processing(ICDIP 2017).2017.
[17]NOOTHOUT J M H,DE V B D,WOLTERINK J M,et al.CNN-based Landmark Detection in Cardiac CTA Scans[J].ar-Xiv:1804.04963,2018.
[18]LIU T,DONG A,XU W,et al.Study on extracting reflectance just beneath water surface from remote sensing reflectance[C]//Geoinformatics.International Society for Optics and Photonics,2007.
[19]RAYCHAUDHURI B ,ROY S.Martian Atmospheric Spectral Radiance Used as Model for Water Vapor Correction of Terrestrial Carbon Dioxide Absorption Profile Around 2 μm[J].IEEE Geoscience and Remote Sensing Letters,2021:18-10.
[20]WANG J,YANG C,SUN C.A Novel Algorithm for Edge Detection of Remote Sensing Image Based on CNN and PSO[C]//2009 2nd International Congress on Image and Signal Proces-sing.IEEE,2009.

相关文章 15

[1]	周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085
[2]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[3]	徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[4]	饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[5]	宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[6]	汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[7]	王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074
[8]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[9]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[10]	孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[11]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[12]	闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042
[13]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[14]	王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[15]	郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于改进多层卷积神经网络的水体富营养化遥感监测算法研究

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

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0