Knowledge-guided classification of coastal zone color images off the West Florida Shelf

A knowledge-guided approach to automatic classification of Coastal Zone Col or images off the West Florida Shelf is described. The approach is used to identify red tides on the West Florida Shelf, as well as areas with high co ncentration of dissolved organic matter such as a river plume found seasona lly along the West Florida coast over the middle of the shelf. The Coastal Zone Color images are initially segmented by the unsupervised Multistage Ra ndom Sampling Fuzzy c-Means algorithm. Then, a knowledge-guided system is a pplied to the centroid values of resultant clusters to label case I, case I I waters, a dilute river plume ("green river"), and red tide. The domain kn owledge base contains information on cluster distribution in feature space, as well as spatial information such as bathymetry data. Our knowledge base consists of a rule-guided system and an embedded neural network. From 60 i mages, after training the system, this procedure recognizes all 15 images w hich contained a river plume and 45 images without. The system can correctl y classify 74% of the pixels that belong to the river plume, which provides a substantial advantage to users looking for offshore extensions of riveri ne influence. Red tides are also successfully identified in a time series o f images for which ground truth confirmed the presence of a harmful bloom.