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

Abstract

For change detection in remote sensing, constructing a training dataset for deep learning models is quite 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. However, training with unpaired dataset shows not enough performance compared with other methods based on bi-temporal supervision. We suspect this phenomenon caused by ignorance of meaningful information in the actual bi-temporal pairs.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. Our method achieves state-of-the-art performance in a large gap than existing methods.