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Proceedings Paper

Convolution neural networks for ship type recognition
Author(s): Katie Rainey; John D. Reeder; Alexander G. Corelli
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Paper Abstract

Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.

Paper Details

Date Published: 12 May 2016
PDF: 11 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 984409 (12 May 2016); doi: 10.1117/12.2229366
Show Author Affiliations
Katie Rainey, Space and Naval Warfare Systems Ctr. Pacific (United States)
John D. Reeder, Space and Naval Warfare Systems Ctr. Pacific (United States)
Alexander G. Corelli, Space and Naval Warfare Systems Ctr. Pacific (United States)

Published in SPIE Proceedings Vol. 9844:
Automatic Target Recognition XXVI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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