Stochastic clutter characterization in NASA/JPL AIRSAR imagery

The background or clutter in SAR imagery has a significant stochastic component. It is often desirable to be able to rapidly characterize the clutter distribution and/or classify the background type based on the clutter distribution. We model the stochastic clutter as a piecewise stationary random held. The individual stationary subregions of homogeneity in the held can then be characterized by marginal density functions. This level of characterization is often sufficient for determination of clutter type on a local basis. We present a technique for the simultaneous characterization of the subregions of a random field based on semiparametric density estimation on the entire random field. This technique is based on a borrowed strength methodology that allows the use of observations from potentially dissimilar subregions to improve local density estimation and hence random process characterization. This approach is demonstrated on a set of NASA/JPL AIRSAR images, including an example of clutter dependent crash site detection.