Model-based covariance mean variance classification techniques: algorithm development and application to the acoustic classification of zooplankton Academic Article uri icon

abstract

  • For inversion problems in which the theoretical relationship between observed data and model parameters is well characterized, a promising approach to the classification problem is the application of techniques that capitalize on the predictive power of class-specific models. Theoretical models have been developed for three zooplankton scattering classes (hard elastic-shelled, e.g., pteropods; fluid-like, e,g,, euphausiids; and gas-bearing, e.g., siphonophores), providing a sound basis for model-based classification approaches. The Covariance Mean Variance Classification (CMVC) techniques classify broad-band echoes from individual zooplankton based on comparisons of observed echo spectra to model space realizations. Three different CMVC algorithms were developed: the Integrated Score Classifier, the Pairwise Score Classifier, and the Bayesian Probability Classifier; these classifiers assign observations to a class based on similarities in covariance, mean, and variance while accounting for model space ambiguity and validity. The CMVC techniques were applied to broad-band (similar to 350-750 kHz) echoes acquired from 24 different zooplankton to invert for scatterer class and properties. All three classification algorithms had a high rate of success with high-quality high SNR data. Accurate acoustic classification of zooplankton species has the potential to significantly improve estimates of zooplankton biomass made from ocean acoustic backscatter measurements.

publication date

  • 1998