Automated pattern recognition of rotated sea surface temperature features using sequential satellite images

A fast and reliable technique has been developed for establishing sea surface velocities in sequential satellite infrared images using a multiprocessor computer system. The maximum cross correlation (MCC) approach has been used to automate the process of determining the amount of feature movement over time. The amount of feature movement, and the time interval between images, determine the surface velocity. As the water patterns observed tend to exhibit rotational movement as well as translation, the MCC method has been extended to detect rotational motions. In general, the correlation coefficient must be computed for all possible translations and rotations of the template within the search area. Depending on the size of the template in use. A small amount of template rotation may have little or no effect on the correlation coefficient. It is accordingly possible to use an adaptive approach to minimise correlation searches. The system implemented suggests that the tracking of rotating sea features is possible on an automated basis