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Proceedings Paper

Improved hidden Markov model classifier for SAR images
Author(s): Chanin Nilubol; Russell M. Mersereau; Mark J. T. Smith
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Paper Abstract

The study reported in this paper represents a continuation of our previous work on the application of Hidden Markov models (HMMs) to the translational and rotational invariant classification of SAR targets. The traditional method of making classification decisions using an HMM does not achieve the desired objective of minimizing the number of misclassifications. We present a novel technique that minimizes the probability of misclassification error. This approach, which is an adaptation of an existing Minimum Classification Error strategy, is globally optimal. The proposed method applies basic principles of pattern recognition to reduce the expected misclassification rate by dynamically perturbing the HMM parameters using a constraint on a cross-entropy measure and distance separation between pairs of HMM models. Like the traditional implementation of an HMM, our new formulation can still be implemented using an efficient forward-backward algorithm for estimating the HMM parameters. We tested our classifier on a public mixed- target MSTAR database and compared our approach to the original HMM approach trained by using a maximum likelihood criterion. The results indicate a significant improvement over the original HMM approach. Current scores from our method are about in excess of 90% on testing data sets.

Paper Details

Date Published: 27 July 1999
PDF: 10 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357150
Show Author Affiliations
Chanin Nilubol, Georgia Institute of Technology (United States)
Russell M. Mersereau, Georgia Institute of Technology (United States)
Mark J. T. Smith, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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