Bayesian seabed classification using angle-dependent backscatter data from multibeam echo sounders

Abstract

Acoustical seabed classification is a technology for mapping seabed sediments. Processed multibeam sonar data yield the variation of the seabed scattering strength with incidence angle, and this paper examines the effect of this on classification. A simple Gaussian statistical model is developed for the observed scattering strength, whereby an observation is represented by a piecewise constant function of incidence angle. Provided some data for which the sediment types are known (training data), the statistics for each type can be robustly estimated. Subsequently, a standard Bayesian theory is applied to classify new observations. The model was used to compute limits on classification accuracy in terms of the intrinsic scattering strength statistics of the seabed, and to predict whether a logarithmic or linear scale for the data is preferable. Systematic experiments on a North Sea data set with four sediment classes tested how the classification accuracy depends on the piecewise function approximation, incidence angle range, amount of training data, and spatial averaging (combining consecutive pings into one observation). The classifier based on Gaussian statistics performed at least as well as sophisticated algorithms with no assumptions about the data statistics. The best accuracy (95%) was attained for logarithmic data. The amount of training data needed to achieve this was about 500 pings per class; spatial averaging could be limited to 10-20 pings. Comparable across-track spatial resolution was possible by dividing the full swath into separate independent sectors, but only at reduced accuracy (87% or less). However, comparable accuracy may be possible by taking into account the spatial relationships of observations.