  Most change detection methods assume that pre-change and post-change images
are acquired by the same sensor. However, in many real-life scenarios, e.g.,
natural disaster, it is more practical to use the latest available images
before and after the occurrence of incidence, which may be acquired using
different sensors. In particular, we are interested in the combination of the
images acquired by optical and Synthetic Aperture Radar (SAR) sensors. SAR
images appear vastly different from the optical images even when capturing the
same scene. Adding to this, change detection methods are often constrained to
use only target image-pair, no labeled data, and no additional unlabeled data.
Such constraints limit the scope of traditional supervised machine learning and
unsupervised generative approaches for multi-sensor change detection. Recent
rapid development of self-supervised learning methods has shown that some of
them can even work with only few images. Motivated by this, in this work we
propose a method for multi-sensor change detection using only the unlabeled
target bi-temporal images that are used for training a network in
self-supervised fashion by using deep clustering and contrastive learning. The
proposed method is evaluated on four multi-modal bi-temporal scenes showing
change and the benefits of our self-supervised approach are demonstrated.
