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In this paper, we consider the problem of change detection (CD) with twoheterogeneous remote sensing (RS) images. For this problem, an unsupervisedchange detection method has been proposed recently based on the imagetranslation technique of Cycle-Consistent Adversarial Networks (CycleGANs),where one image is translated from its original modality to the modality of theother image so that the difference map can be obtained by performingarithmetical subtraction. However, the difference map derived from subtractionis susceptible to image translation errors, in which case the changed area andthe unchanged area are less distinguishable. To overcome the above shortcoming,we propose a new unsupervised copula mixture and CycleGAN-based CD method(COMIC), which combines the advantages of copula mixtures on statisticalmodeling and the advantages of CycleGANs on data mining. In COMIC, thepre-event image is first translated from its original modality to thepost-event image modality. After that, by constructing a copula mixture, thejoint distribution of the features from the heterogeneous images can be learntaccording to quantitive analysis of the dependence structure based on thetranslated image and the original pre-event image, which are of the samemodality and contain totally the same objects. Then, we model the CD problem asa binary hypothesis testing problem and derive its test statistics based on theconstructed copula mixture. Finally, the difference map can be obtained fromthe test statistics and the binary change map (BCM) is generated by K-meansclustering. We perform experiments on real RS datasets, which demonstrate thesuperiority of COMIC over the state-of-the-art methods.

COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote  Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial  Networks