A NOVEL FRAMEWORK FOR BI-TEMPORAL CHANGE DETECTION IN IMAGE TIME SERIES

Change detection (CD) between a pair of images is a popular problem in remote sensing. Despite a large amount of data is acquired every day by remote sensing satellites, standard CD methods usually consider only the two target images between which we desire to detect changes. The aim of this work is to present a novel framework in which the bi-temporal CD is redefined by evaluating the consistency of the changes occurred in the target image pair with all the other changes of images within the considered time series. Our approach evaluates pixel-wise the changes in temporal closed-loops that include the two target images where the resulting binary change/no-change sequences can be processed by strategies inspired to the error-control-coding theory. Unreliable CD results for the target images can be identified and corrected. The experimental results on both a synthetic and a real dataset demonstrate the effectiveness of the proposed framework.