5 May 2006 New experiments in the use of support vector machines in polarimetric radar target classification
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Firooz A. Sadjadi, "New experiments in the use of support vector machines in polarimetric radar target classification", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340X (5 May 2006); doi: 10.1117/12.669792; https://doi.org/10.1117/12.669792


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