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.

翻译：我们研究了空间点过程中存在杂波时的特征检测问题。已有研究开发了多种分类方法。其中，Byers与Raftery（1998）将观测到的Kth最近邻距离建模为混合分布，并据此对杂波点与特征点进行分类。本文从两个方面对该方法进行了改进。首先，我们提出了一种自动选择分类方法中所用最近邻数量的程序，该程序基于分段回归模型实现。其次，为通过多次应用该程序获得"更优"的最终结果，我们提出了一种停止准则，该准则能够最小化杂波点与特征点之间聚类分离度的整体熵度量。所提出的方法适用于任意空间（包括线性网络）上作为两个叠加泊松过程的特征与杂波场景。我们通过模拟实验和两个环境数据案例研究对该方法进行了验证。