Automatic detection of meddies through texture analysis of sea surface temperature maps

A new machine learning approach is presented for automatic detection of Mediterranean water eddies from sea surface temperature maps of the Atlantic Ocean. A pre-processing step uses Laws' convolution kernels to reveal microstructural patterns of water temperature. Given a map point, a numerical vector containing information on local structural properties is generated. This vector is forwarded to a multi-layer perceptron classifier that is trained to recognise texture patterns generated by positive and negative instances of eddy structures. The proposed system achieves high recognition accuracy with fast and robust learning results over a range of different combinations of statistical measures of texture properties. Detection results are characterised by a very low rate of false positives. The latter is particularly important since meddies occupy only a small portion of SST map area.