Vessel classification in overhead satellite imagery using learned dictionaries

Katie Rainey; Shibin Parameswaran; Josh Harguess; John Stastny

doi:10.1117/12.928875

15 October 2012 Vessel classification in overhead satellite imagery using learned dictionaries

Katie Rainey, Shibin Parameswaran, Josh Harguess, John Stastny

Proceedings Volume 8499, Applications of Digital Image Processing XXXV; 84992F (2012) https://doi.org/10.1117/12.928875
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

Recognition and classification of vessels in maritime imagery is a challenging problem with applications to security and military scenarios. Aspects of this problem are similar to well-studied problems in object recognition, but it is in many ways more complex than a problem such as face recognition. A vessel's appearance can vary significantly from image to image depending on factors such as lighting condition, viewing geometry, and sea state, and there is often wide variation between ships of the same class. This paper explores the efficacy of several object recognition algorithms at classifying ships and other ocean vessels in commercial panchromatic satellite imagery. The recognition algorithms tested include traditional classification methods as well as more recent methods utilizing sparse matrix representations and dictionary learning. The impacts on classification accuracy of various pre-processing steps on vessel imagery are explored, and we discuss how these algorithms are being used in existing systems to detect and classify vessels in satellite imagery.

Citation Download Citation

Katie Rainey, Shibin Parameswaran, Josh Harguess, and John Stastny "Vessel classification in overhead satellite imagery using learned dictionaries", Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 84992F (15 October 2012); https://doi.org/10.1117/12.928875

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