  Synthetic aperture sonar (SAS) systems produce high-resolution images of the
seabed environment. Moreover, deep learning has demonstrated superior ability
in finding robust features for automating imagery analysis. However, the
success of deep learning is conditioned on having lots of labeled training
data, but obtaining generous pixel-level annotations of SAS imagery is often
practically infeasible. This challenge has thus far limited the adoption of
deep learning methods for SAS segmentation. Algorithms exist to segment SAS
imagery in an unsupervised manner, but they lack the benefit of
state-of-the-art learning methods and the results present significant room for
improvement. In view of the above, we propose a new iterative algorithm for
unsupervised SAS image segmentation combining superpixel formation, deep
learning, and traditional clustering methods. We call our method Iterative Deep
Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework
that can be divided into four main steps: 1) A deep network estimates class
assignments. 2) Low-level image features from the deep network are clustered
into superpixels. 3) Superpixels are clustered into class assignments (which we
call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for
loss backpropagation of the deep network prediction. These four steps are
performed iteratively until convergence. A comparison of IDUS to current
state-of-the-art methods on a realistic benchmark dataset for SAS image
segmentation demonstrates the benefits of our proposal even as the IDUS incurs
a much lower computational burden during inference (actual labeling of a test
image). Finally, we also develop a semi-supervised (SS) extension of IDUS
called IDSS and demonstrate experimentally that it can further enhance
performance while outperforming supervised alternatives that exploit the same
labeled training imagery.
