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}

翻译：在传统的遥感变化检测（RS CD）流程中，通常需要首先对双时相图像进行大量的人工标注，以保证后续全监督训练的性能。然而，为CD任务进行像素级标注非常复杂且耗时。本文探索了一种适用于RS CD任务的新型自监督对比学习框架，该框架通过领域适配器和层次化对比头，促使模型准确捕捉空间、结构及语义信息。所提出的SSLChange框架仅需单时相样本即可完成自学习，并能灵活迁移至主流CD基线模型。通过自监督对比学习，即使在没有标注的情况下，也可以直接基于原始数据进行特征表示预训练。在后续获得一定数量的标注后，预训练的特征将与标注对齐，进行全监督微调。在不引入任何额外数据或标注的情况下，下游基线模型的性能将得到显著提升。在两个完整数据集和六个稀释数据集上的实验结果表明，我们提出的SSLChange在数据受限的情况下提升了CD基线模型的性能和稳定性。SSLChange的代码将在 \url{https://github.com/MarsZhaoYT/SSLChange} 发布。