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Change detection for remote sensing images is widely applied for urban changedetection, disaster assessment and other fields. However, most of the existingCNN-based change detection methods still suffer from the problem of inadequatepseudo-changes suppression and insufficient feature representation. In thiswork, an unsupervised change detection method based on Task-relatedSelf-supervised Learning Change Detection network with smooth mechanism(TSLCD)is proposed to eliminate it. The main contributions include: (1) thetask-related self-supervised learning module is introduced to extract spatialfeatures more effectively. (2) a hard-sample-mining loss function is applied topay more attention to the hard-to-classify samples. (3) a smooth mechanism isutilized to remove some of pseudo-changes and noise. Experiments on four remotesensing change detection datasets reveal that the proposed TSLCD methodachieves the state-of-the-art for change detection task.

Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection