Simulation sea surface current from RADARSAT-2 SAR data using Hopfield neural network

Neural networks have been applied successfully to pattern recognition, stereo vision, motion analysis and object tracking problems. This work utilizes a new approach of neural network for modeling coastal current pattern from RADARSAT-2 SAR data. The Hopfield neural network was used to model sea surface current movements. In matching process using Hopfield neural network, identified features have to be mathematically compared to each other in order to build an energy function that could be minimized. In this regard, the neuron network has been taken in two dimensions; raw and column in to match between the similar features of surface pattern, and it was required that the two features were extracted from the same location. The Euler method is used to minimized the energy function of neuron equation. The study shows that the surface current pattern can be modeled by high accuracy of ±0.2 m/s.