Bayesian classification of surface-based ice-radar images

This paper deals with the application of the Bayes classification procedure to discriminate types of sea ice based on images obtained from surface-based marine radars. The data sets were digitized images obtained from a dual-polarized -band radar (16 GHz) and a like-polarized -band radar (3 GHz) at a site located on the northern tip of Baffin Island, Canada. The images were first range-compensated, and statistical properties of different ice types were then determined. The observed histograms for different ice types were approximated by continuous density functions. The images were classified by maximizing the a posteriori probabilities obtained from Bayes\´s rule. The results reported herein suggest that there is sufficient information in the reflectivity to classify the different forms of ice using decision-theoretic pattern recognition techniques.