A novel circular approach to change detection in pair of images extracted from image time series

This paper presents a novel approach to binary change detection in pairs of images extracted from time series. The main idea is that, given a binary change detection map obtained with any literature technique applied to the considered pair of images, we can identify possible change detection errors exploiting other images in the time series. This can be done by considering other pairs of images in the time series that, jointly with the analyzed one, can define a closed circular path in time. Then we model the binary change variable as a conservative field along circular paths within the time series. If for a pixel the circular path does not satisfy the conservativeness property an error is detected. Accordingly, the change detection label on that pixel is considered unreliable Experimental results obatined on a time series of ASAR Envisat images point out the effectiveness of the approach in detecting unreliable pixels.