  Object classification in synthetic aperture sonar (SAS) imagery is usually a
data starved and class imbalanced problem. There are few objects of interest
present among much benign seafloor. Despite these problems, current
classification techniques discard a large portion of the collected SAS
information. In particular, a beamformed SAS image, which we call a single-look
complex (SLC) image, contains complex pixels composed of real and imaginary
parts. For human consumption, the SLC is converted to a magnitude-phase
representation and the phase information is discarded. Even more problematic,
the magnitude information usually exhibits a large dynamic range (>80dB) and
must be dynamic range compressed for human display. Often it is this dynamic
range compressed representation, originally designed for human consumption,
which is fed into a classifier. Consequently, the classification process is
completely void of the phase information. In this work, we show improvements in
classification performance using the phase information from the SLC as well as
information from an alternate source: photographs. We perform statistical
testing to demonstrate the validity of our results.
