Convolution neural networks for ship type recognition

Katie Rainey; John D. Reeder; Alexander G. Corelli

doi:10.1117/12.2229366

12 May 2016 Convolution neural networks for ship type recognition

Katie Rainey, John D. Reeder, Alexander G. Corelli

Proceedings Volume 9844, Automatic Target Recognition XXVI; 984409 (2016) https://doi.org/10.1117/12.2229366
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Katie Rainey, John D. Reeder, and Alexander G. Corelli "Convolution neural networks for ship type recognition", Proc. SPIE 9844, Automatic Target Recognition XXVI, 984409 (12 May 2016); https://doi.org/10.1117/12.2229366

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