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Proceedings Paper

Maritime vessel recognition in degraded satellite imagery
Author(s): Katie Rainey; Shibin Parameswaran; Josh Harguess
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Paper Abstract

When object recognition algorithms are put to practice on real-world data, they face hurdles not always present in experimental situations. Imagery fed into recognition systems is often degraded by noise, occlusions, or other factors, and a successful recognition algorithm must be accurate on such data. This work investigates the impact of data degradations on an algorithm for the task of ship classification in satellite imagery by imposing such degradation factors on both training and testing data. The results of these experiments provide lessons for the development of real-world applications for classification algorithms.

Paper Details

Date Published: 13 June 2014
PDF: 6 pages
Proc. SPIE 9090, Automatic Target Recognition XXIV, 909004 (13 June 2014); doi: 10.1117/12.2049868
Show Author Affiliations
Katie Rainey, Space and Naval Warfare Systems Ctr. Pacific (United States)
Shibin Parameswaran, Space and Naval Warfare Systems Ctr. Pacific (United States)
Josh Harguess, Space and Naval Warfare Systems Ctr. Pacific (United States)

Published in SPIE Proceedings Vol. 9090:
Automatic Target Recognition XXIV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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