Classification of maritime vessels using capsule networks

Capsule networks have shown promise in their ability to perform classification tasks with viewpoint invariance; outperforming the accuracy of other models in some cases. This capability applies to maritime classification tasks where there is a lack of labeled data and an inability to collect all viewpoints of objects that are needed to train machine learning algorithms. Capsule Networks lend themselves well to applying their unique network architecture to the maritime vessel BCCT dataset, which exhibits characteristics aligned with the theorized strengths of Capsule Networks. Comparing these with respect to traditional CNN architectures and data augmentation techniques provides a potential roadmap for incorporation into future classification tasks involving imagery in data starved domains relying heavily on viewpoint invariance. We present our results on the classification of ship using Capsule Networks and explore their usefulness at this task given their current state of development.