Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 194-199.doi: 10.11896/jsjkx.200800142

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Algal Bloom Discrimination Method Using SAR Image Based on Feature Optimization Algorithm

WU Lin1,2,3, BAI Lan1,3,4, SUN Meng-wei 1,3,4, GOU Zheng-wei1,3,4   

  1. 1 College of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China
    2 College of Environment and Planning,Henan University,Kaifeng,Henan 475004,China
    3 Henan Engineering Research Center of Intelligent Technology and Application,Henan University,Kaifeng,Henan 475004,China
    4 Key Laboratory of Analysis and Processing on Big Data of Henan Province,Henan University,Kaifeng,Henan 475004,China
  • Received:2020-08-21 Revised:2020-10-30 Online:2021-09-15 Published:2021-09-10
  • About author:WU Lin,born in 1978,associate professor,master supervisor.Her main research interests include SAR image processing techniques,and SAR image application of water environment.
    GUO Zheng-wei,born in 1963,professor,master supervisor.Her main research interests include SAR image processing techniques,and SAR image application of ecological environment.
  • Supported by:
    National Natural Science Foundation of China(61871175),College Key Research Project of Henan Province(19A420005,21A520004),Plan of Science and Technology of Henan Province(202102210175,212102210093,212102210101) and Key Laboratory of Land Satellite Remote Sensing Application,Ministry of Natural Resources of the People's Republic of China(KLSMNR-202102)

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Abstract: The frequent outbreak of algal bloom in inland lakes has seriously affected the safety of surface water environment,and has brought great obstacles to the construction of ecological civilization in China.Taking full advantage of SAR(Synthetic Aperture Radar) remote sensing technologies,large-scale and periodic algal bloom discrimination and monitoring can be realized.It is of great practical significance for the protection and supervision of water environment.Based on the research and application of SAR remote sensing target recognition technology,this paper proposes an algal bloom discrimination method with feature optimization.After the in-depth analysis and extraction of algal bloom image features,the ReliefF algorithm is used to obtain the optimal feature set,which consists of 10 features from all 22 algal bloom features.And then,the BP (Back Propagation) neural network is as the classifier of this discrimination method to carry out a number of comparative experiments.The overall accuracy of the proposed method is 81.39%,which is 19.38% higher than that before optimization.The experimental results show that the optimal feature set can not only greatly reduce the algorithm complexity,but also effectively improve the discrimination accuracy of algal bloom,which has practical value for further promotion.

Key words: Algal bloom discrimination, Feature optimization, ReliefF algorithm, Synthetic aperture radar image, Water ecology

CLC Number: 

  • TP751
[1]TAO F.Air-water CO2 flux in an algae bloom year for Lake Hongfeng,Southwest China:implications for the carbon cycle of global inland waters[J].Acta Geochimica,2017,36(4):658-666.
[2]KATRI R,JOSÉ E C B,MARIA H K V.Identifying Multiple Stressors that Influence Eutrophication in a Finnish Agricultural River[J].Science of the Total Environment,2019,658:1278-1292.
[3]XIE P.Cyanobacteria bloom and its secondary damage[J].Journal of Hydroecology,2015,36(4):1-13.
[4]GUO L E.Doing battle with the green monster of Taihu Lake[J].Science,2007,317(5842):1166.
[5]GUO H D,ZHANG L.60 years of radar remote sensing:Fourstage development.Journal of Remote Sensing[J].Journal of Remote Sensing,2019,23(6):1023-1035.
[6]SUN W W,YANG G,CHEN C,et al.Development status and literature analysis of China's earth observation remote sensing satellites[J].Journal of Remote Sensing,2020,24(5):4-25.
[7]CHEN L F,LIU Y Z,ZHANG P,et al.Road Extraction Algorithm of Multi-feature High-resolution SAR Image Based on Multi-Path RefineNet[J].Computer Science,2020,47(3):156-161.
[8]WANG H,CHEN X,TIAN S Z,et al.SAR Image Recognition Based on Few-shot Learning[J].Computer Science,2020,47(5):124-128.
[9]PENG J X,SU Y Q,XUE X R.SAR Image Feature Retrieval Method Based on Deep Learning and Synchronic Matrix[J].Computer Science,2019,46(6):196-199.
[10]WANG J,WANG S X,YAN F L,et al.Algal bloom range extraction based on ASAR and MODIS images[J].Remote Sen-sing Information,2009,3:54-57.
[11]LIU J,WANG W C.Cyanobacteria bloom extraction based on ASAR and MODIS remote sensing data[J].Journal of Anhui Agricultural Sciences,2013,41(2):889-892.
[12]BRESCIANI M,ADAMO M,DE CAROLIS G,et al.Monitoring blooms and surface accumulation of cyanobacteria in the Curo-nian Lagoon by combining MERIS and ASAR data[J].Remote Sensing of Environment,2014,146:124-135.
[13]WANG G L.Active and passive remote sensing monitoring of cyanobacteria blooms in inland waters[D].Shanghai:East China Normal University,2015.
[14] WU L,WANG L,MIN L,et al.Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China[J].Remote Sensing,2018,10(5):767.
[15]MERA D,BOLON-CANEDO V,COTOS J M,et al.On the Use of Feature Selection to Improve the Detection of Sea Oil Spills in SAR Images[J].Computers and Geosciences,2017,100:166-178.
[16]RAEISI A,AKBARIZADEH G,MAHMOUDI A.CombinedMethod of an Efficient Cuckoo Search Algorithm and Nonnegative Matrix Factorization of Different Zernike Moment Features for Discrimination between Oil Spills and Lookalikes in SAR Images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(11):4193-4205.
[17]JOHANNESSEN J A,SCHUCHMAN R A,DIGRANCES G,et al.Coastal Ocean Fronts and Eddies Imaged with ERS-1 Synthetic Aperture Radar[J].Journal of Geophysical Research,1996,101:6651-6667.
[18]KONG F X,GAO G.Reflection on the formation mechanism of cyanobacteria bloom in large shallow eutrophication lakes[J].Acta Ecologica Sinica,2005(3):589-595.
[19]KONG F X,MA R H,GAO J F,et al.Theory and Practice of prevention,prediction and early warning of cyanobacteria bloom in Taihu Lake[J].Journal of Lake Sciences,2009(3):10-24.
[20]BAI X H,HU W P,HU Z X,et al.Calculation of wind drift vo-lume of sheet floating algal bloom in Meiliang Bay,Taihu Lake in summer 2004[J].Environmental Science,2005,26(6):57-60.
[21]ZHOU Y Y,CHENG J H,LIU T,et al.Review of Road Extraction for High-resolution SAR Images[J].Computer Science,2020,47(1):124-135.
[22]GUYON I M,GUNN S R,NIKRAVESH M,et al.Feature Extraction:Foundations and Applications[J].Studies in Fuzziness &Soft Computing,2006,205(12):68-84.
[23]KONONENKO I.Estimating attributes:Analysis and exten-sions of RELIEF[C]//European Conference on Machine Lear-ning on Machine Learning. Springer,1994:171-182.
[24]DUAN H,MA R,XU X,et al.Two-Decade Reconstruction of Algal Blooms in China's Lake Taihu[J].Environmental Scien-ce &Technology,2012,43(10):3522-3528.
[25]CHEN Y W,QIN B Q,TEUBNER K,et al.Long-term dyna-mics of phytoplankton assemblages:Microcystis-domination in Lake Taihu,a large shallow lake in China[J].Journal of Plankton Research,2003,25(1):445-453.
[26]YANG L Y,YANG X Y,REN L M,et al.Outbreak mechanism and control measures of cyanobacteria bloom in Taihu Lake[J].Journal of Lake Sciences,2019,31(1):18-27.
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