Detecting features in spatial point processes with clutter via model-based clustering

We consider the problem of detecting features, such as minefields or seismic faults, in spatial point processes when there is substantial clutter. We use model-based clustering based on a mixture model for the process, in which features are assumed to generate points according to highly linear multivariate normal densities, and the clutter arises according to a spatial Poisson process. Nonlinear features are represented by several densities, giving a piecewise linear representation. Hierarchical model-based clustering provides a first estimate of the features, and this is then refined using the EM algorithm. The number of features is estimated from an approximation to its posterior distribution. The method gives good results for the minefield and seismic fault problems. Software to implement it is available on the World Wide Web.