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

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. High resolution sonar imagery are analysed using a mixture model consisting of K distributions. The number of K distributions is chosen using several model selection techniques and compared with the authors own interpretation of the data.