用高分辨率影像增强低分辨率遥感图像的自监督学习效果
Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation
- 引入空间关联模块，利用高分辨影像提升低分辨图像表征
- 在两个自监督框架上均优于仅用高/低分辨率数据预训练的模型
- 适合遥感图像语义分割任务，尤其关注多尺度数据融合
遥感领域的自监督预训练主要依赖于高可用性的中分辨率（MR）图像数据集。随着高分辨率（HR）数据集的发布，我们探讨如何将HR数据纳入自监督预训练，以增强针对MR图像的表征学习和下游语义分割性能。为此，设计了一个可嵌入现有自监督学习框架的空间关联组件，利用HR影像来学习更优的MR影像表示。在两个自监督学习框架上测试该组件，结果表明其表现优于仅使用HR或仅使用MR图像预训练的模型。
原文摘要 · Abstract (English)
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.
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