SSLChange: A Self-Supervised Change Detection Framework Based on Domain Adaptation

In conventional remote sensing change detection (RSCD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training. However, pixel-level labeling for change detection (CD) tasks is very complex and time-consuming. In this article, we explore a novel self-supervised contrastive framework applicable to the RSCD task, which promotes the model to accurately capture spatial, structural, and semantic information through the domain adapter (DA) and the hierarchical contrastive head. The proposed SSLChange framework accomplishes self-learning only by taking a single-temporal sample and can be flexibly transferred to mainstream CD baselines. With self-supervised contrastive learning, feature representation pretraining can be performed directly based on the original data even without labeling. After a certain number of labels are subsequently obtained, the pretrained features will be aligned with the labels for fully supervised fine-tuning. Without introducing any additional data or labels, the performance of downstream baselines will experience a significant enhancement. Experimental results on two entire datasets and six diluted datasets show that our proposed SSLChange improves the performance and stability of CD baseline in data-limited situations. The code of SSLChange is available at https://github.com/MarsZhaoYT/SSLChange