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13 March 2003 Clutter and target characterization using Markov chains
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In this paper a new approach for clutter and target characterization is proposed. The method is based on the use of Markov chains for representing the samples of both the clutter and the target. The mathematical representation of the clutter and the target is based on the transition matrix of an irreducible Markov chain. This kind of representation incorporates a full description of the underlying pdf as well as any order of statistical correlation. Among the useful and meaningful parameters of the transition matrix are its eigenvalues. In natural signals, transition matrices have only a small number of their elements with significant value. This fact can be used to device relatively simple Markov chain models for clutter representation. The target statistics can also be modeled by means of a Markov chain model. However, in this case, the model may be simpler since the target samples or pixels are highly correlated and their values are restricted to a smaller range compared to those of the clutter.
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Vassilis Anastassopoulos and George A. Lampropoulos "Clutter and target characterization using Markov chains", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003);

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