  Remote-sensing (RS) Change Detection (CD) aims to detect "changes of
interest" from co-registered bi-temporal images. The performance of existing
deep supervised CD methods is attributed to the large amounts of annotated data
used to train the networks. However, annotating large amounts of remote sensing
images is labor-intensive and expensive, particularly with bi-temporal images,
as it requires pixel-wise comparisons by a human expert. On the other hand, we
often have access to unlimited unlabeled multi-temporal RS imagery thanks to
ever-increasing earth observation programs. In this paper, we propose a simple
yet effective way to leverage the information from unlabeled bi-temporal images
to improve the performance of CD approaches. More specifically, we propose a
semi-supervised CD model in which we formulate an unsupervised CD loss in
addition to the supervised Cross-Entropy (CE) loss by constraining the output
change probability map of a given unlabeled bi-temporal image pair to be
consistent under the small random perturbations applied on the deep feature
difference map that is obtained by subtracting their latent feature
representations. Experiments conducted on two publicly available CD datasets
show that the proposed semi-supervised CD method can reach closer to the
performance of supervised CD even with access to as little as 10% of the
annotated training data. Code available at https://github.com/wgcban/SemiCD
