Automatic Detection of Oceanic Eddies in SeaWiFS-Derived Color Images Using Neural Networks and Shape Analysis

Key remote sensing instruments like advanced Sea-viewing Wide Field-of-view Sensor (SeaWiFS) aboard satellites, play a vital role in collecting observations that help in analyzing the properties of oceans around the globe. This research focuses on analysis and processing of high-resolution chlorophyll (ocean color) observations from SeaWiFS to automatically identify and segment ocean features. An oceanic eddy is a circular or elliptical whirling flow of water generally found along the edge of a dominant current. Mesoscale eddies are those vortices, whose diameters ranges from a few kilometers in the coastal ocean to a few hundred kilometers in the deeper open ocean. The objective of this work is to use neural network and shape analysis techniques to automatically detect and segment oceanographic eddies from chlorophyll color images. The focus of this research is on the Monterey Bay region off the California coast.