Hopfield neural network and pareto optimal algorithms for retrieving sea surface current from TanDEM-X data

Abstract

This is first work is done on the application of TanDEM-X data satellite data for the Malaysian coastal waters. This study aims at comparison between Hopfield neural network and Pareto optimal algorithms for modelling sea surface current using TanDEM-X satellite data. In fact, X-band data have a great potential for retrieving sea surface parameters such as sea surface current movement and ocean wave spectra. Therefore, TanDEM-X is the term of the satellite operation hovering the two satellites in a strictly controlled foundation with regular distances between 250 and 500 m. The study of ocean surface current is important for understanding the coastal water circulation. The set of TanDEM-X satellite data are examined by using Hopfield neural network algorithm. The sea surface current is retrieved based on the energy function. Therefore, the Pareto optimal algorithm is used to determine the optimal solution for nonlinearity problem which is raised due to the Doppler frequency shift impact. The study shows that the Pareto optimum resolution has highest performance than Hopfield neural network rule with lowest RMSE of ±0.08. Further, Pareto optimum resolution can verify the ocean surface current pattern variation on coastal water from TanDEMX data. Last, TanDEMX data reveals a superb guarantees for retrieving ocean surface current with X-band.