Self-supervised pretraining in remote sensing is mostly done using mid-spatial resolution (MR) image datasets due to their high availability. Given the release of high-resolution (HR) datasets, we ask how HR datasets can be included in self-supervised pretraining to enhance MR image representation learning and downstream segmentation performance on MR tasks. We design a spatial affinity component that can be added to existing self-supervised learning frameworks and that uses HR imagery to learn better representations of MR imagery. We test the spatial affinity component on two self-supervised learning frameworks and show that it outperforms models pretrained on HR or MR images alone.

翻译：遥感领域的自监督预训练主要利用中空间分辨率影像数据集进行，这得益于其较高的可获取性。鉴于高分辨率数据集的发布，我们探讨如何将高分辨率数据集纳入自监督预训练，以增强中分辨率影像的表征学习及其在中分辨率任务下游分割性能。我们设计了一个可嵌入现有自监督学习框架的空间亲和力组件，该组件利用高分辨率影像来学习中分辨率影像的更好表征。我们在两种自监督学习框架上测试了该空间亲和力组件，结果表明其性能优于单独使用高分辨率或中分辨率影像预训练的模型。