Adaptive Block-Based Change-Point Detection for Sparse Spatially Clustered Data with Applications in Remote Sensing Imaging

TL;DR

This paper introduces a non-parametric, adaptive block-based change-point detection method designed for high-dimensional, spatially clustered data, with applications in remote sensing to identify small, localized changes over time.

Contribution

It proposes a novel change-point detection framework that accounts for spatial dependencies and improves detection power in sparse, high-dimensional datasets.

Findings

Superior detection of sparse changes demonstrated in simulations

Effective identification of activity in remote sensing imagery

Enhanced estimation accuracy through spatial dependency modeling

Abstract

We present a non-parametric change-point detection approach to detect potentially sparse changes in a time series of high-dimensional observations or non-Euclidean data objects. We target a change in distribution that occurs in a small, unknown subset of dimensions, where these dimensions may be correlated. Our work is motivated by a remote sensing application, where changes occur in small, spatially clustered regions over time. An adaptive block-based change-point detection framework is proposed that accounts for spatial dependencies across dimensions and leverages these dependencies to boost detection power and improve estimation accuracy. Through simulation studies, we demonstrate that our approach has superior performance in detecting sparse changes in datasets with spatial or local group structures. An application of the proposed method to detect activity, such as new construction, in remote sensing imagery of the Natanz Nuclear facility in Iran is presented to demonstrate the method's efficacy.