A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms

An approach for selecting and blending bio-optical algorithms is demonstrat ed using an ocean color satellite image of the northwest Atlantic shelf. Th is approach is based on a fuzzy logic classification scheme applied to the satellite-derived water-leaving radiance data, and it is used to select and blend class-specific algorithms. Local in situ bio-optical data were used to characterize optically-distinct water classes a priori and to parameteri ze algorithms for each class. Although the algorithms can be of any type (e mpirical or analytical), this demonstration involves class-specific semi-an alytic algorithms, which are the inverse of a radiance model. The semi-anal ytic algorithms retrieve three variables related to the concentrations of o ptically active constituents. When applied to a satellite image, the fuzzy logic approach involves three steps. First, a membership function is comput ed for each pixel and each class. This membership function expresses the li kelihood that the measured radiance belongs to a class with a known reflect ance distribution. Thus, for each pixel, class memberships are assigned to the predetermined classes on the basis of the derived membership functions. Second, three variables are retrieved from each of the class-specific algo rithms for which the pixel has membership. Third, the class memberships are used to weight the class-specific retrievals to obtain a final blended ret rieval for each pixel. This approach allows for graded transitions between water types, and blends separately tuned algorithms for different water mas ses without suffering from the "patchwork quilt" effect associated with har d-classification schemes.