Ship classification from overhead imagery using synthetic data and domain adaptation

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 othe...

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Bibliographic Details
Main Authors Ward, Chris M, Harguess, Josh, Hilton, Cameron
Format Journal Article
LanguageEnglish
Published 09.05.2019
Subjects
Computer Science - Computer Vision and Pattern Recognition
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Summary: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.
DOI:10.48550/arxiv.1905.03894