Abstract
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.
Abstract (translated)
在遥感领域的自监督预训练中，通常使用的是中等空间分辨率（MR）图像数据集，因为它们的高可用性。鉴于高分辨率（HR）数据集的发布，我们探讨了如何将这些HR数据集纳入自监督预训练过程，以增强对MR图像表示的学习，并提升下游分割任务中的表现性能。为此，我们设计了一个可以添加到现有自监督学习框架中的空间亲和力组件，该组件利用HR影像来学习更好的MR影像表示。我们在两个自监督学习框架中测试了这个空间亲和力组件，并展示了它在仅使用HR或MR图像预训练的模型上表现更佳的结果。
URL
https://arxiv.org/abs/2601.12964
