KNOWLEDGE-GUIDED CLASSIFICATION OF COASTAL ZONE COLOR IMAGES OFF THE WEST FLORIDA SHELF

Abstract

A knowledge-guided approach to automatic classification of Coastal Zone Color 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 concentration of dissolved organic matter such as a river plume found seasonally along the West Florida coast over the middle of the shelf. The Coastal Zone Color images are initially segmented by the unsupervised Multistage Random Sampling Fuzzy c-Means algorithm. Then, a knowledge-guided system is applied to the centroid values of resultant clusters to label case I, case II waters, a dilute river plume ("green river"), and red tide. The domain knowledge 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 images, after training the system, this procedure recognizes all 15 images which contained a river plume and 45 images without. The system can correctly classify 74% of the pixels that belong to the river plume, which provides a substantial advantage to users looking for offshore extensions of riverine influence. Red tides are also successfully identified in a time series of images for which ground truth confirmed the presence of a harmful bloom.