Research On Oceanic Mesoscale Eddy Extraction And Tracking Method Based On Deep Learning

Oceanic mesoscale eddies have a significant impact on the transport of energy materials and acoustic propagation,as well as the ecological system and climate in the nearby area.The rapid and accurate identification of mesoscale eddy position and its tracking and trajectory prediction are of great value in the fields of physical oceanography,Marine biology and Marine acoustics.However,due to the absence of accurate definition of eddy itself,the extraction method with both accuracy and rapidity is lacking.The most accurate extraction method of ocean mesoscale eddy relies on "expert visual interpretation" of ocean remote sensing images.However,the widely used traditional detection methods rely too much on the threshold and have great subjectivity.The existing machine learning methods are not mature enough,the methods are single in function and the training set is not authoritative.In view of the above problems,this paper USES deep learning technology to carry out automatic extraction and tracking research of Marine mesoscale eddies.The main work is as follows:1.A Marine mesoscale eddy automatic detection and positioning method based on data enhancement and target detection network is proposed.The method based on remote sensing data,provided by the AVISO sea using auxiliary data with image processing technology to enhance Marine experts to generate the training set,and then build to the depth of the residual network with the characteristics of the pyramid network as the main body of the ocean-OEDNet mesoscale eddy automatic detection model,finally through the closed contour method for eddy core positioning and eddy region extraction.Experimental results show that compared with the existing methods,this method can achieve better eddy detection effect,and the model has a good generalization ability.2.An automatic tracking method of ocean mesoscale eddy based on long and shortterm memory network is proposed.Based on the OEDNet detection results and the existing eddy track data set,the visualization image of sea surface variables is drawn.The features of the sea surface data field are extracted by using convolutional neural network,and the mesoscale eddy motion Angle prediction model,OETNet,is constructed with the long and short-term memory network as the main body.Based on the results of Angle assist and eddy detection,mesoscale eddy automatic tracking is realized.The experimental results show that the proposed method is more accurate than the existing method.3.Based on the above work,an OEDNet and OETNet based oceanic mesoscale eddy automatic extraction and tracking software is designed and implemented.The software consists of data preprocessing and data enhancement module,eddy detection module and eddy tracking module.