Unsupervised Change Detection Based on Image Reconstruction Loss

Abstract

To train a 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 an unsupervised change detection method based on image reconstruction loss, which uses only a single-temporal unlabeled image. The image reconstruction model was trained to reconstruct the original source image by receiving the source image and photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as input and aims to re-construct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector demonstrated significant performance on various change detection benchmark datasets even though only a single-temporal source image was used. The code and trained models are available in https://github.com/cjf8899/CDRL