Luttrell S P, October 1987, Designing Markov random field structures for clutter modelling, Proceedings of International Conference on Radar (London, UK), IEE Conference Publication (, ed. ), vol. , pp. 222-226
A thorough understanding of clutter statistics is a prerequisite for the successful analysis of radar images. Usually very simple statistics such as moments and correlation properties are used, perhaps based on an underlying physical model of the scattering and imaging process. In this paper we use the maximum entropy method to reconstruct clutter probability density functions (PDF) from observed statistical properties; this leads to representations of clutter in terms of Markov random fields (MRF). Furthermore we show how the set of statistics which is used for each clutter type may be optimised in order to yield a more compact probabilistic model. The principal advantage of our results is that MRF clutter models may be mapped directly onto parallel image processing hardware, and they provide a rigorous framework for Bayesian decision making concerning the presence of objects embedded in clutter. Image segmentation is another very useful application of these MRF models.