  In quantitative analysis of seafloor imagery, it is common to model the
collection of individual pixel intensities scattered by the seafloor as a
random variable with a given statistical distribution. There is a considerable
literature on statistical models for seafloor scattering, mostly focused on
areas with statistically homogeneous properties (i.e. exhibiting spatial
stationarity). For more complex seafloors, the pixel intensity distribution is
more appropriately modeled using a mixture of simple distributions. For very
complex seafloors, fitting 3 or more mixture components makes physical sense,
but the statistical model becomes much more complex in these cases. Therefore,
picking the number of components of the mixture model is a decision that must
be made, using a priori information, or using a data driven approach. However,
this information is time consuming to collect, and depends on the skill and
experience of the human. Therefore, a data-driven approach is advantageous to
use, and is explored in this work. Criteria for choosing a model always need to
balance the trade-off for the best fit for the data on the one hand and the
model complexity on the other hand. In this work, we compare several
statistical model selection criteria, e.g., the Bayesian information criterion.
Examples are given for SAS data collected by an autonomous underwater vehicle
in a rocky environment off the coast of Bergen, Norway using data from the
HISAS-1032 synthetic aperture sonar system.
