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Automatic target recognition using discrimination based on optimal transport
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0001-5158-9255
2015 (English)In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, IEEE conference proceedings, 2015, 2604-2608 p.Conference paper (Refereed)Text
Abstract [en]

The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra. In particular, spectral estimation methods based on ℓ1 regularization as well as covariance based methods can be shown to be robust with respect to such distances. These transportation distances provide a geometric framework where geodesics corresponds to smooth transition of spectral mass, and have been useful for tracking. In this paper we investigate the use of these distances for automatic target recognition. We study the use of the Monge-Kantorovich distance compared to the standard ℓ2 distance for classifying civilian vehicles based on SAR images. We use a version of the Monge-Kantorovich distance that applies also for the case where the spectra may have different total mass, and we formulate the optimization problem as a minimum flow problem that can be computed using efficient algorithms.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 2604-2608 p.
Keyword [en]
Automatic target recognition, Optimal transport, Power spectra, SAR
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-181578DOI: 10.1109/ICASSP.2015.7178442ScopusID: 2-s2.0-84946031982ISBN: 9781467369978OAI: diva2:912997
40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015, 19 April 2014 through 24 April 2014

QC 20160318

Available from: 2016-03-18 Created: 2016-02-02 Last updated: 2016-03-18Bibliographically approved

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Karlsson, Johan Mikael
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