A hybrid approach to acoustic classification and length estimation of krill

The problem of acoustically identifying and estimating the sizes of two euphausiid species is considered. A euphausiid aggregation is represented by a three-dimensional probabilistic vector whose components are the mean ratios of the volume backscattering coeffi cients, measured at two different frequencies. The decisions on the species and the relative misclassifi cation errors are calculated individually for each component of a vector, using classical Bayesian techniques. Classification probabilities are derived by integrating the individual decisions. The size structure of the classifi ed aggregation is derived from a fluid-sphere model. The effectiveness of the method is demonstrated by comparing the acoustic estimates of species and sizes to net samples collected during three surveys conducted in the Ross Sea under various environmental conditions.