Weakly-Supervised Semantic Segmentation of Circular-Scan,
  Synthetic-Aperture-Sonar Imagery
Isaac J. Sledge Dominic M. Byrne Jonathan L. King Steven H. Ostertag Denton L. Woods James L. Prater Jermaine L. Kennedy Timothy M. Marston Jose C. Principe
We propose a weakly-supervised framework for the semantic segmentation of
circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our
framework is trained in a supervised manner, on image-level labels, to uncover
a set of semi-sparse, spatially-discriminative regions in each image. The
classification uncertainty of each region is then evaluated. Those areas with
the lowest uncertainties are then chosen to be weakly labeled segmentation
seeds, at the pixel level, for the second part of the framework. Each of the
seed extents are progressively resized according to an unsupervised,
information-theoretic loss with structured-prediction regularizers. This
reshaping process uses multi-scale, adaptively-weighted features to delineate
class-specific transitions in local image content. Content-addressable memories
are inserted at various parts of our framework so that it can leverage features
from previously seen images to improve segmentation performance for related
images.
  We evaluate our weakly-supervised framework using real-world CSAS imagery
that contains over ten seafloor classes and ten target classes. We show that
our framework performs comparably to nine fully-supervised deep networks. Our
framework also outperforms eleven of the best weakly-supervised deep networks.
We achieve state-of-the-art performance when pre-training on natural imagery.
The average absolute performance gap to the next-best weakly-supervised network
is well over ten percent for both natural imagery and sonar imagery. This gap
is found to be statistically significant.