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Proceedings Paper

Classification of sequenced SAR target images via hidden Markov models with decision fusion
Author(s): Timothy W. Albrecht; Kenneth W. Bauer
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Paper Abstract

The classification ground vehicle targets from the MSTAR (Moving and Stationary Target Acquisition and Recognition) database is investigated using Gaussian-mixture hidden Markov models (gHMMs) and synthetic aperture radar images. The gHMMs employ features extracted from High Range Resolution (HRR) radar signal magnitude versus range profiles of the targets. Feature enhancement is made using Cetin's point-based reconstruction technique. The impact on classification accuracy across numbers of hidden states and sequence length is explored using separate training and testing data. Multiple gHMM classifier outputs are fused according to various decision rules across which classification performance is explored.

Paper Details

Date Published: 19 May 2005
PDF: 8 pages
Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.603694
Show Author Affiliations
Timothy W. Albrecht, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5808:
Algorithms for Synthetic Aperture Radar Imagery XII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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