  The vast amount of unlabeled multi-temporal and multi-sensor remote sensing
data acquired by the many Earth Observation satellites present a challenge for
change detection. Recently, many generative model-based methods have been
proposed for remote sensing image change detection on such unlabeled data.
However, the high diversities in the learned features weaken the discrimination
of the relevant change indicators in unsupervised change detection tasks.
Moreover, these methods lack research on massive archived images. In this work,
a self-supervised change detection approach based on an unlabeled multi-view
setting is proposed to overcome this limitation. This is achieved by the use of
a multi-view contrastive loss and an implicit contrastive strategy in the
feature alignment between multi-view images. In this approach, a pseudo-Siamese
network is trained to regress the output between its two branches pre-trained
in a contrastive way on a large dataset of multi-temporal homogeneous or
heterogeneous image patches. Finally, the feature distance between the outputs
of the two branches is used to define a change measure, which can be analyzed
by thresholding to get the final binary change map. Experiments are carried out
on five homogeneous and heterogeneous remote sensing image datasets. The
proposed SSL approach is compared with other supervised and unsupervised
state-of-the-art change detection methods. Results demonstrate both
improvements over state-of-the-art unsupervised methods and that the proposed
SSL approach narrows the gap between unsupervised and supervised change
detection.
