  In this paper, we consider the problem of change detection (CD) with two
heterogeneous remote sensing (RS) images. For this problem, an unsupervised
change detection method has been proposed recently based on the image
translation technique of Cycle-Consistent Adversarial Networks (CycleGANs),
where one image is translated from its original modality to the modality of the
other image so that the difference map can be obtained by performing
arithmetical subtraction. However, the difference map derived from subtraction
is susceptible to image translation errors, in which case the changed area and
the 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 statistical
modeling and the advantages of CycleGANs on data mining. In COMIC, the
pre-event image is first translated from its original modality to the
post-event image modality. After that, by constructing a copula mixture, the
joint distribution of the features from the heterogeneous images can be learnt
according to quantitive analysis of the dependence structure based on the
translated image and the original pre-event image, which are of the same
modality and contain totally the same objects. Then, we model the CD problem as
a binary hypothesis testing problem and derive its test statistics based on the
constructed copula mixture. Finally, the difference map can be obtained from
the test statistics and the binary change map (BCM) is generated by K-means
clustering. We perform experiments on real RS datasets, which demonstrate the
superiority of COMIC over the state-of-the-art methods.
