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Proceedings Paper

New experiments in the use of support vector machines in polarimetric radar target classification
Author(s): Firooz A. Sadjadi
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Paper Abstract

This paper summarizes the results of experiments in developing Support Vector Machines for polarimetric radar target classification. Previous studies have shown that proper selection of state of polarization in both transmitting and receiving stages can noticeably improve target classification performace. Polarization syntheses is used to generate radar signatures of several targets at various transmit/receive pairs of polarization angles. Then statistical attributes from each radar signature are used for its reperesentation. To address the target separation ambiguities, support vector machines using a number of kernels are developed and used. The results of applying this approach on real fully polarimetric radar data indicate that only a small subset of polarization angles are sufficient for generating signatures needed for training a classifier for optimal separation of targets.

Paper Details

Date Published: 5 May 2006
PDF: 10 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 62340X (5 May 2006); doi: 10.1117/12.669792
Show Author Affiliations
Firooz A. Sadjadi, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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