With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms. Few pretext-based algorithms, including from rotation, spatial context and jigsaw puzzles are not appropriate for satellite images. Often, satellite images have a higher temporal frequency. So, the temporal dimension of remote sensing data provides natural augmentation without requiring us to create artificial augmentation of images. Here, we propose S3-TSS, a novel method of self-supervised learning technique that leverages natural augmentation occurring in temporal dimension. We compare our results with current state-of-the-art methods and also perform various experiments. We observed that our method was able to perform better than baseline SeCo in four downstream datasets. Code for our work can be found here: https://github.com/hewanshrestha/Why-Self-Supervision-in-Time

翻译：由于遥感图像中标注数据在不同大气条件下的有限可用性，利用自监督算法开展工作显得尤为必要。现有基于预文本的算法，包括旋转、空间上下文和拼图等，并不适用于卫星图像。卫星图像通常具有较高的时间频率。因此，遥感数据的时间维度提供了天然的数据增强，无需我们额外创造人工图像增强。在此，我们提出S3-TSS，一种新型自监督学习技术方法，该方法利用时间维度上的天然增强。我们将结果与当前最先进的方法进行比较，并进行了多项实验。我们观察到，在四个下游数据集中，我们的方法优于基线SeCo。本工作的代码可在以下链接获取：https://github.com/hewanshrestha/Why-Self-Supervision-in-Time