2 September 2004 Hidden Markov models for classifying SAR target images
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Proceedings Volume 5427, Algorithms for Synthetic Aperture Radar Imagery XI; (2004); doi: 10.1117/12.541454
Event: Defense and Security, 2004, Orlando, Florida, United States
Abstract
The classification of three types of ground vehicle targets from the MSTAR (Moving and Stationary Target Acquisition and Recognition) database is investigated using hidden Markov models (HMMs) and synthetic aperture radar images. The HMMs employ training sets of six power spectrum features extracted from High Range Resolution (HRR) radar signal magnitude versus range profiles of the targets for uniform sequences of aspect angles (7 degree separation). Classification accuracy versus numbers of hidden states (from 3 to 30), sequence length (3, 10, 15, and 30), and discretization level of the features (10 and 30 levels) is explored using test and validation data. Best classification (94% correct) is achieved for 3 hidden states, a sequence length of 30, and 10 feature levels.
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Timothy W Albrecht, Steven C Gustafson, "Hidden Markov models for classifying SAR target images", Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.541454; https://doi.org/10.1117/12.541454
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KEYWORDS
Synthetic aperture radar

Target recognition

Expectation maximization algorithms

Stochastic processes

Target acquisition

Feature extraction

Radar

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