Ship classification by superstructure moment invariants

Authors:
Prashan Premaratne;Farzad Safaei
Affiliations:
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia;School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

Direct observation using satellites and long range video surveillance is not possible for ship classification in adverse weather and during night. Radar and more specifically radar imaging offers a solution for the above adverse conditions. Ship Classification using radar is of utmost important to defense of any country to manage vast naval resources and to tell the friend from foe. Automatic ship classification based on radar images has been very successful in determining the ship class as well as other details to reliably recognize a ship type using machine vision. Inverse Synthetic Aperture Radar (ISAR) Imaging which relies on a stationary radar and a moving object with preferably superstructure will result in an image that is somewhat unique to a particular ship class. There have been many attempts to classify these ISAR images automatically with varying degree of success. The results we present here using Moment Invariants (Hu Moments) are indeed superior to many other feature-based classification approaches as they have strong invariant properties.