A Novel Change Point Detection Method for Data Cubes of Satellite Image Time Series

Following the launch of the Copernicus Sentinels, which has enabled the free access to petabytes of satellite data, change point analysis has caught the attention of the remote sensing community. In fact, the exploitation of satellite image time series has a number of advantages over the use of an image pair, as it allows a better understanding of how the process under study is evolving. Although this is a well-known area of research that spans different application domains, the majority of the change point detection methods have been designed for the analysis of univariate signal, and only a few of them can be used to process multidimensional data. In this paper, we present a novel change point detection method based on the combination of wavelets and mathematical morphology for the analysis of data cubes of satellite image time series that allows the user to reduce the data dimensionality at the input level. We conducted a preliminary performance assessment on 50 sites in Belgium using up to 5 different input features derived from Sentinel-1 and Sentinel-2 data.