Prediction method of remote sensing data based on fourier neural operator
Remote sensing of the earth is the main task of meteorological satellites.Due to factors such as cloud cover and cosmic ray radiation,remote sensing data obtained by meteorological satellites often have a large number of missing and ab-normal data.Fourier neural operators have the characteristics of high efficiency,high accuracy,and flexible resolution.This pa-per proposes a remote sensing data prediction algorithm based on Fourier neural operators.The algorithm first fills in the miss-ing values of remote sensing data using the spatial mean method and Lagrange interpolation method,and then trains the map-ping relationship of spatial values in the time domain using Fourier neural operators.Finally,the trained model is used to pre-dict the latest remote sensing data.Simulation experiments based on real remote sensing data of Fengyun-4 remote sensing sat-ellite show that the method proposed in this paper is more effective than others.Good prediction accuracy can still be main-tained in long-term time series forecasting.
earth remote sensingtime series predictionlong-term predictionfourier neural operator
万方数据
维普
