Multiple angle acoustic classification of zooplankton.

The use of multiple angle acoustic scatter to discriminate between two taxa of fluid-like zooplankton, copepods and euphausiids, is explored. Using computer modeling, feature extraction, and subsequent classification, the accuracy in discriminating between the two taxa is characterized via computer simulations. The model applies the distorted wave Born approximation together with a simple system geometry, a linear array, to predict a set of noisy training and test data. Three feature spaces are designed, exploiting the relationship between the shape of the scatterer and angularly varying scattering amplitude, to extract discriminant features from these data. Under the assumption of uniform random length and uniform three-dimensional orientation distributions for each class of scatterers, the performance of several classification algorithms is evaluated. Simulations reveal that the incorporation of multiple angle data leads to a marked improvement in classification performance over single angle methods. The improvement is more substantial using broadband scatter. The simulations indicate that under the stated assumptions, a low classification error can be obtained. The use of multiple angle scatter therefore holds promise to substantially improve the in situ acoustic classification of fluid-like zooplankton using simple observation geometries.