  We consider the problem of feature detection in the presence of clutter in
spatial point processes. Classification methods have been developed in previous
studies. Among these, Byers and Raftery (1998) models the observed Kth nearest
neighbour distances as a mixture distribution and classifies the clutter and
feature points consequently. In this paper, we enhance such approach in two
manners. First, we propose an automatic procedure for selecting the number of
nearest neighbours to consider in the classification method by means of
segmented regression models. Secondly, with the aim of applying the procedure
multiple times to get a ``better" end result, we propose a stopping criterion
that minimizes the overall entropy measure of cluster separation between
clutter and feature points. The proposed procedures are suitable for a feature
with clutter as two superimposed Poisson processes on any space, including
linear networks. We present simulations and two case studies of environmental
data to illustrate the method.
