While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).

中文翻译：

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自我监督的预训练可增强Sentinel-2图像中的变化检测

尽管使用卫星图像进行带注释的更改检测的图像非常稀少且获取成本很高，但是每天都会生成大量未标记的图像。为了利用这些数据来学习更适合于变化检测的图像表示，我们探索了利用Sentinel-2时序的时间一致性来获得可用的自我监督学习信号的方法。为此，我们建立了Sentinel-2多时相城市对（S2MTCP）数据集，并将其公开发布（https://zenodo.org/record/4280482），其中包含来自全球1520个城市地区的多时相图像对。我们测试了用于变化检测的预训练模型的多种自我监督学习方法的结果，并将其应用于由Sentinel-2图像对（OSCD）构成的公共变化检测数据集。