Circular change detection in image time series inspired by two-dimensional phase unwrapping

A sheer amount of data is collected everyday by a large variety of remote sensing sensors and new acquisitions are continuously added to data records of existing Earth Observation archives. The unprecedented amount of information acquired on a study area can be useful for many remote sensing applications, but requires the solution of Big Data challenges. Based on these observations, the Circular change detection has been proposed as a novel framework that redefines the bi-temporal change detection problem to take advantage of a full image time series. It evaluates the binary change variable in circular closed paths where it is a conservative quantity. In this paper, a strategy inspired by the 2D phase unwrapping is applied to this conservative variable to locate CD errors in pairs of images within the time series. The effectiveness of the proposed approach is established by experimental results obtained on synthetic and real datasets.