  Multimodal and multisensor data analysis is a long-standing goal in machine
learning research. In this paper we revisit multisensor analysis in context of
self-supervised change detection in bi-temporal satellite images. 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. While optical images are
like the natural images dealt in computer vision, SAR images appear vastly
different 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 trained network is evaluated on multi-modal
satellite data showing change and the benefits of our self-supervised approach
are demonstrated.
