Finding Green River in SeaWiFS Satellite Images

Understanding oceanic primary production on a global scale can be enhanced by methods that are able to automatically track phytoplankton blooms from color satellite images. In this paper, unsupervised clustering and rule learning are combined to track green river, a plume of discolored water that forms every March-May offshore along the edge of the west Florida Shelf, from the Sea Viewing Wide Field of View Sensor which beganjying in late 1997. Spatial information and sea surface temperature can be integrated into the approach to improve pe$ormance. Using crossvalidation experiments over a series of 59 multi-spectral images, it is shown that the developed system is able to reliably discriminate between images with green river from those with no phytoplankton blooms or other kinds of blooms. It is also effective in identihing the region which the green river covers.