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Proceedings Paper

Translational- and rotational-invariant hidden Markov model for automatic target recognition
Author(s): Chanin Nilubol; Quoc Henry Pham; Russell M. Mersereau; Mark J. T. Smith; Mark A. Clements
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Paper Abstract

This paper discusses the application of Hidden Markov Models (HMMs) to the Automatic Target Recognition (TRI-ATR) problem in Synthetic Aperture Radar (SAR) imagery. Related research with applications of the HMMs to solve SAR Automatic Target Recognition (ATR) problems can also be found in Kottke et al. Our approach is based on a cascade of three stages: preprocessing, feature extraction and selection, and classification. Preprocessing and feature extraction and selection involve operations performed on the Radon transform of target chips. The features, which are invariant to changes in rotation, position and shifts, although not to changes in scale, are optimized through the use of feature selection techniques. The classification stage takes as its inputs the multidimensional multiple observation sequences and parameterizes them statistically using continuous density models to capture the target and background appearance variability. Experimental results have demonstrated that the recognition rate can be as high as 95% over both the training set and the testing set, in certain cases.

Paper Details

Date Published: 17 July 1998
PDF: 7 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327095
Show Author Affiliations
Chanin Nilubol, Georgia Institute of Technology (United States)
Quoc Henry Pham, 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)
Mark A. Clements, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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