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Optical Engineering

Feature-based target recognition with a Bayesian network
Author(s): Jun Liu; Kuo-Chu Chang
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Paper Abstract

The problem of target classification with high-resolution, fully polarimetric, synthetic aperture radar (SAR) imagery is considered. We propose a framework of using a Bayesian network for feature fusion to deal with the difficult problem of SAR target classification. One difficult problem in SAR feature identification and fusion for target classification is that the features identified may not be independent and that it is not easy to find the ‘‘right’’ fusion rule to combine them. The Bayesian network model when constructed properly can explicitly represent the conditional independence and dependence between various features and therefore provide a sound and natural framework for feature fusion. This paper summarizes our recent work in SAR target recognition using a feature-based Bayesian inference approach. The approach works on the selected features which are chosen so that the separability of the original data are well maintained for later classification. Once the original data are mapped into feature space, the probabilistic model between features and the target is estimated and represented by a Bayesian network, which is then used to calculate the probabilities that a target belongs to one of the given classes based on the observed features. A comparison between the above technique and the traditional statistical approaches such as nearest mean and Fisher pairwise is illustrated based upon performance on a fully polarimetric ISAR (inverse SAR) image data set. Note that although the feature set used in the paper is obtained from the same sensor, the concepts of feature selection and Bayesian network formulation discussed in the paper are not restricted to this case only. They can be applied for multisensor feature-level fusion as well.

Paper Details

Date Published: 1 March 1996
PDF: 7 pages
Opt. Eng. 35(3) doi: 10.1117/1.600638
Published in: Optical Engineering Volume 35, Issue 3
Show Author Affiliations
Jun Liu, George Mason Univ. (United States)
Kuo-Chu Chang, George Mason Univ. (United States)

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