Accession Number : ADP003125

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


Personal Author(s) : Coulter,R. E.

Report Date : OCT 1983

Pagination or Media Count : 11

Abstract : 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)

Descriptors :   *Statistical decision theory, *Ocean surface, *Water masses, *Classification, Gulf Stream, Bayes theorem, North Atlantic Ocean, Surface temperature, Statistical data, Oceanographic data

Distribution Statement : APPROVED FOR PUBLIC RELEASE