  To train the change detector, bi-temporal images taken at different times in
the same area are used. However, collecting labeled bi-temporal images is
expensive and time consuming. To solve this problem, various unsupervised
change detection methods have been proposed, but they still require unlabeled
bi-temporal images. In this paper, we propose unsupervised change detection
based on image reconstruction loss using only unlabeled single temporal single
image. The image reconstruction model is trained to reconstruct the original
source image by receiving the source image and the photometrically transformed
source image as a pair. During inference, the model receives bi-temporal images
as the input, and tries to reconstruct one of the inputs. The changed region
between bi-temporal images shows high reconstruction loss. Our change detector
showed significant performance in various change detection benchmark datasets
even though only a single temporal single source image was used. The code and
trained models will be publicly available for reproducibility.
