Model selection techniques for seafloor scattering statistics in synthetic aperture sonar images of complex seafloors

Abstract

In quantitative analysis of seafloor scattering measurements, it is common to model the single-point probability density function of the scattered intensity or amplitude. For more complex seafloors, the pixel amplitude distribution has previously been modelled with a mixture model consisting of two K distributions, but the environment may have more identifiable scattering mechanisms. Choosing the number of components of a mixture model is a decision that must be made, using a priori information, or using a data driven approach. Several common model selection techniques from the statistics literature are explored (the Akaike, Bayesian, deviance, and Watanabe-Akaike information criteria) and compared to the authors' choice. Examples are given for synthetic aperture sonar data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway, using the HISAS-1032 synthetic aperture sonar system. The Bayesian information criterion aligned most closely with the interpretation of both the acoustic images and the plots of the probability of false alarm.