  In conventional remote sensing change detection (RS CD) 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 CD tasks is very complex and time-consuming. In this paper, we
explore a novel self-supervised contrastive framework applicable to the RS CD
task, which promotes the model to accurately capture spatial, structural, and
semantic information through domain adapter and hierarchical contrastive head.
The proposed SSLChange framework accomplishes self-learning only by taking a
single-temporal sample and can be flexibly transferred to main-stream CD
baselines. With self-supervised contrastive learning, feature representation
pre-training can be performed directly based on the original data even without
labeling. After a certain amount of labels are subsequently obtained, the
pre-trained 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 2 entire datasets and 6 diluted datasets show that our proposed
SSLChange improves the performance and stability of CD baseline in data-limited
situations. The code of SSLChange will be released at
\url{https://github.com/MarsZhaoYT/SSLChange}
