Research On Deep-Ocean Remote Sensing By Deep Learning

The traditional ship-based or buoy observation can obtain hydrological parameters of the ocean surface with high accuracy,but it is difficult to obtain marine dynamic environmental parameters with relatively comprehensive spatial distribution.Satellite remote sensing can provide high-temporal coverage of sea surface wind,effective wave height,sea surface height,sea surface flow and temperature and salt and other marine dynamic environment parameters,but lacks medium and deep ocean dynamic parameters.With the continuous development and maturity of deep learning,more and more disciplines and applications have begun to utilize the deep learning technology.Therefore,this paper aims to use deep learning,inversion of the dynamic parameters of the ocean surface inversion by remote sensing satellites,and ship-based buoy data,to invert the deep global dynamic parameters data set with high precision and complete space-time coverage.On the other hand,the ocean dynamic process mechanism is complicated,and traditional physical models are difficult to accurately simulate and predict.The prediction of seawater temperature has important applications in many marine sciences.Previous studies on seawater temperature prediction have focused on the temperature of surface seawater and have less prediction of seawater temperature changes at different depths.Therefore,this paper will try to use the deep learning method to predict the time series of seawater temperature at different depths.This paper uses the temperature,salinity and sea surface height of the ocean surface to invert the temperature and salinity information of the middle and deep seawater based on BP neural network.On the other hand,this paper combines Long Short Term Memory network(LSTM)with Convolutional Neural Network(CNN),utilizing the time-series sea-temperature characteristics of different depths to train the neural network and predict seawater temperature data at different depths in a certain time step.The mid-deep inversion method based on the BP network can invert the seawater temperature information in the middle and deep layers with high precision by the SST,SSH,SSS and other parameters of the sea surface obtained by satellite remote sensing.The multi-layer convLSTM prediction method can predict seawater temperature information at different depths and has better accuracy in a certain time step.