  For change detection in remote sensing, constructing a training dataset for
deep learning models is difficult due to the requirements of bi-temporal
supervision. To overcome this issue, single-temporal supervision which treats
change labels as the difference of two semantic masks has been proposed. This
novel method trains a change detector using two spatially unrelated images with
corresponding semantic labels such as building. However, training on unpaired
datasets could confuse the change detector in the case of pixels that are
labeled unchanged but are visually significantly different. In order to
maintain the visual similarity in unchanged area, in this paper, we emphasize
that the change originates from the source image and show that manipulating the
source image as an after-image is crucial to the performance of change
detection. Extensive experiments demonstrate the importance of maintaining
visual information between pre- and post-event images, and our method
outperforms existing methods based on single-temporal supervision. code is
available at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.
