Computer Vision for Ocean Eddy Detection in Infrared Imagery

Reliable and precise detection of ocean eddies can significantly improve the monitoring of the ocean surface and subsurface dynamics, besides the characterization of local hydrographical and biological properties, or the concentration pelagic species. Today, most of the eddy detection algorithms operate on satellite altimetry gridded observations, which provide daily maps of sea surface height and surface geostrophic velocity. However, the reliability and the spatial resolution of altimetry products is limited by the strong spatio-temporal averaging of the mapping procedure. Yet, the availability of high-resolution satellite imagery makes real-time object detection possible at a much finer scale, via advanced computer vision methods. We propose a novel eddy detection method via a transfer learning schema, using the ground truth of high-resolution ocean numerical models to link the characteristic streamlines of eddies with their signature (gradients, swirls, and filaments) on Sea Surface Temperature (SST). A trained, multi-task convolutional neural network is then employed to segment infrared satellite imagery of SST in order to retrieve the accurate position, size, and form of each detected eddy. The EddyScan-SST is an operational oceanographic module that provides, in real-time, key information on the ocean dynamics to maritime stakeholders.