Water Mass Classification in the North Atlantic Using IR Digital Data and Bayesian Decision Theory

• Main Author: Coulter,R. E.
• Other Authors: NAVAL OCEANOGRAPHIC OFFICE NSTL STATION MS
• Format: Text
• Language: English
• Published: 1983
• Subjects: *Statistical decision theory; *Ocean surface; *Water masses; *Classification; Gulf Stream; Bayes theorem; North Atlantic Ocean; Surface temperature; Statistical data; Oceanographic data; Component Reports; Infrared digital data; North Atlantic
• Online Access: http://www.dtic.mil/docs/citations/ADP003125; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADP003125

A method is described which utilizes Bayesian decision theory and historical statistics of sea surface temperature to classify surface water masses and ocean fronts from satellite-derived infrared data. Probabilities that certain features occur are determined from the normal distributions of specific statistical characteristics, known a priori, and the same characteristics computed from satellite data. The better the match between the a priori information associated with a feature and the computed statistics, the higher the probability that the feature exists. The maximum probability determined by Baye's theory is subjected to two tests, based on absolute and relative threshold values, to reduce the chance of incorrect classification. The method was used for classifying satellite IR data to locate the major water masses in the Gulf Stream region. Results were compared to frontal positions obtained by conventional, subjective means. (Author) This article is from the PAME Proceedings, Pattern Analysis in the Marine Environment, an Ocean Science and Technology Workshop Held at the Naval Ocean Research and Development Activity, NSTL, MS. on 24-26 Mar 82, AD-A140 195, p225-236.