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[bibtex]@InProceedings{Seo_2023_WACV, author = {Seo, Minseok and Lee, Hakjin and Jeon, Yongjin and Seo, Junghoon}, title = {Self-Pair: Synthesizing Changes From Single Source for Object Change Detection in Remote Sensing Imagery}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6374-6383} }
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.
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