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For change detection in remote sensing, constructing a training dataset fordeep learning models is difficult due to the requirements of bi-temporalsupervision. To overcome this issue, single-temporal supervision which treatschange labels as the difference of two semantic masks has been proposed. Thisnovel method trains a change detector using two spatially unrelated images withcorresponding semantic labels such as building. However, training on unpaireddatasets could confuse the change detector in the case of pixels that arelabeled unchanged but are visually significantly different. In order tomaintain the visual similarity in unchanged area, in this paper, we emphasizethat the change originates from the source image and show that manipulating thesource image as an after-image is crucial to the performance of changedetection. Extensive experiments demonstrate the importance of maintainingvisual information between pre- and post-event images, and our methodoutperforms existing methods based on single-temporal supervision. code isavailable at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.

Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery