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Ship classification by superstructure moment invariants

Chapter


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.

Publication Date


  • 2009

Citation


  • Premaratne, P. & Safaei, F. 2009, 'Ship classification by superstructure moment invariants', in V. Bevilacqua, D. Huang, K. Jo, H. Kang & H. Lee (eds), Emerging intelligent computing technology and applications : ICIC 2009, Springer, Berlin, Germany. pp. 327-335.

Scopus Eid


  • 2-s2.0-70350393655

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1445

Book Title


  • Emerging intelligent computing technology and applications : ICIC 2009

Has Global Citation Frequency


Start Page


  • 327

End Page


  • 335

Place Of Publication


  • Berlin, Germany

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.

Publication Date


  • 2009

Citation


  • Premaratne, P. & Safaei, F. 2009, 'Ship classification by superstructure moment invariants', in V. Bevilacqua, D. Huang, K. Jo, H. Kang & H. Lee (eds), Emerging intelligent computing technology and applications : ICIC 2009, Springer, Berlin, Germany. pp. 327-335.

Scopus Eid


  • 2-s2.0-70350393655

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1445

Book Title


  • Emerging intelligent computing technology and applications : ICIC 2009

Has Global Citation Frequency


Start Page


  • 327

End Page


  • 335

Place Of Publication


  • Berlin, Germany