Multiview, Broadband Acoustic Classification of Marine Fish: A Machine Learning Framework and Comparative Analysis

Multiview, broadband, acoustic classification of individual fish was investigated using a recently developed laboratory scattering system. Scattering data from nine different species of saltwater fish were collected. Using custom software, these data were processed and filtered to yield a data set of 36 individuals, and between 200 and 500 echoes per individual. These data were sampled uniformly randomly in fish orientation. Feature-, decision-, and collaborative-fusion algorithms were then developed and tested using support vector machines (SVMs) as the underlying classifiers. Decision fusion was implemented by cascading two levels of support vectors machines. Collaborative fusion was implemented by using SVM outputs to estimate confidence levels and performing weighted averaging of probabilities computed from each view with feedback from other views. Collaborative fusion performed as well or better than the others, and did so without requiring assumptions about view geometry. In addition to a comparison between classification algorithms and feature transformations, two data collection geometries were explored, including random observation geometries. In all cases, combining multiple, broadband views yielded significant reductions in classification error (>;50%) over single-view methods, for uniformly random fish orientation.