Advances in Kth nearest-neighbour clutter removal

TL;DR

This paper improves feature detection in cluttered spatial point data by automating neighbor selection and introducing a stopping criterion to enhance classification accuracy, applicable to various spaces including networks.

Contribution

It introduces an automatic neighbor selection method and a stopping criterion to refine clutter removal in spatial point processes, extending previous mixture-based approaches.

Findings

Effective in simulations and environmental case studies.

Automates neighbor selection for better classification.

Reduces entropy to improve clutter-feature separation.

Abstract

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.