  In this paper, we revisit the problem of classifying ships (maritime vessels)
detected from overhead imagery. Despite the last decade of research on this
very important and pertinent problem, it remains largely unsolved. One of the
major issues with the detection and classification of ships and other objects
in the maritime domain is the lack of substantial ground truth data needed to
train state-of-the-art machine learning algorithms. We address this issue by
building a large (200k) synthetic image dataset using the Unity gaming engine
and 3D ship models. We demonstrate that with the use of synthetic data,
classification performance increases dramatically, particularly when there are
very few annotated images used in training.
