  The United States coastline spans 95,471 miles; a distance that cannot be
effectively patrolled or secured by manual human effort alone. Unmanned Aerial
Vehicles (UAVs) equipped with infrared cameras and deep-learning based
algorithms represent a more efficient alternative for identifying and
segmenting objects of interest - namely, ships. However, standard approaches to
training these algorithms require large-scale datasets of densely labeled
infrared maritime images. Such datasets are not publicly available and manually
annotating every pixel in a large-scale dataset would have an extreme labor
cost. In this work we demonstrate that, in the context of segmenting ships in
infrared imagery, weakly-supervising an algorithm with sparsely labeled data
can drastically reduce data labeling costs with minimal impact on system
performance. We apply weakly-supervised learning to an unlabeled dataset of
7055 infrared images sourced from the Naval Air Warfare Center Aircraft
Division (NAWCAD). We find that by sparsely labeling only 32 points per image,
weakly-supervised segmentation models can still effectively detect and segment
ships, with a Jaccard score of up to 0.756.
