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

Music City Center 

We present a non-parametric change-point detection approach for detecting 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 smaller (unknown) subset of dimensions, where the 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 estimation accuracy. Through simulation studies, we demonstrate that our approach has superior performance in detecting sparse changes for datasets with spatial or local group structures. An application of 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.

Change-point

Non-parametric

Spatial Dependence

Graph-based Tests

High-dimensional data

Satellite Imagery 

Section on Statistics in Defense and National Security