The Use of the Hopfield Neural Network to Measure Sea-Surface Velocities From Satellite Images

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The knowledge of ocean surface circulation is of major importance for many applications, including the understanding of global climate, resources exploitation, and containment of chemical spills. In this letter, sea-surface feature tracking based on the Hopfield neural network (NN) is described. The method is based on the minimization of an energy function that represents the feature tracking problem. A Hopfield NN is used to merge cross-correlation information with prior knowledge of sea-surface flows and image contextual information. It has been tested on real satellite images. A set of five Advanced Very High Resolution Radiometer thermal images of the coastal zone of California, along with a data set of coincident surface drifters positions, was used to test the method. Results of the new analysis are compared with <emphasis emphasistype="boldital">in situ</emphasis> data and previous results using other techniques. The method can be used on various kinds of images for tracking and also find other applications in image registration and pattern recognition. </para>