19 May 2005 Classification of sequenced SAR target images via hidden Markov models with decision fusion
Author Affiliations +
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Timothy W. Albrecht, Timothy W. Albrecht, Kenneth W. Bauer, Kenneth W. Bauer, "Classification of sequenced SAR target images via hidden Markov models with decision fusion", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.603694; https://doi.org/10.1117/12.603694
PROCEEDINGS
8 PAGES


SHARE
Back to Top