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Spatio‐temporal classification in point patterns under the presence of clutter
Author(s) -
Siino Marianna,
RodríguezCortés Francisco J.,
Mateu Jorge,
Adelfio Giada
Publication year - 2020
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2599
Subject(s) - clutter , context (archaeology) , k nearest neighbors algorithm , point (geometry) , pattern recognition (psychology) , spatial contextual awareness , euclidean geometry , euclidean distance , mathematics , computer science , expectation–maximization algorithm , temporal database , artificial intelligence , data mining , maximum likelihood , statistics , geography , radar , geometry , telecommunications , archaeology
We consider the problem of detection of features in the presence of clutter for spatio‐temporal point patterns. In previous studies, related to the spatial context, K th nearest‐neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation‐maximization algorithm. This paper extends this methodology to the spatio‐temporal context by considering the properties of the spatio‐temporal K th nearest‐neighbor distances. For this purpose, we make use of a couple of spatio‐temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such K th nearest‐neighbor distances and present an intensive simulation study together with an application to earthquakes.