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In this paper, a novel unsupervised change detection approach based on cross-correlation coefficient is proposed. The cross-correlation coefficient is a measure of the similarity between two variables. The change detection problem can be understood as the process to partition two input images into two distinct regions, namely “changed” and “unchanged”, according to the binary change detection mask. Each region in the pair of the images of the corresponding position is considered as two sets of variables, whose cross-correlation coefficient is calculated in order to provide an optimal partition of the changed and unchanged regions. In the optimal partition, it is obvious that the cross-correlation coefficient of the set of the unchanged variables should be the maximum, while the absolute-value of that of the changed variables should be the minimum, because the corresponding unchanged regions are similar while the changed regions are quite different. Genetic Algorithm is used to obtain the optimal non-dominated solution as the change detection using cross-correlation coefficient is a multi-objective optimization problem. The simulation experiment shows that the result using the new method is effective and robust to radiometric difference.