This work explores capabilities of the pre-trained CLIP vision-language model
to identify satellite images affected by clouds. Several approaches to using
the model to perform cloud presence detection are proposed and evaluated,
including a purely zero-shot operation with text prompts and several
fine-tuning approaches. Furthermore, the transferability of the methods across
different datasets and sensor types (Sentinel-2 and Landsat-8) is tested. The
results that CLIP can achieve non-trivial performance on the cloud presence
detection task with apparent capability to generalise across sensing modalities
and sensing bands. It is also found that a low-cost fine-tuning stage leads to
a strong increase in true negative rate. The results demonstrate that the
representations learned by the CLIP model can be useful for satellite image
processing tasks involving clouds.

该研究探讨了预训练的 CLIP 视觉 - 语言模型在识别受云影响的卫星图像方面的能力。提出并评估了几种使用该模型进行云存在性检测的方法，包括纯零样本操作和几种微调方法。此外，测试了这些方法在不同数据集和传感器类型（Sentinel-2 和 Landsat-8）之间的可迁移性。结果表明，CLIP 在云存在性检测任务上可以实现非平凡的性能，并且具有跨感知模式和感知频带泛化的明显能力。研究还发现，低成本的微调阶段可以显著提高真阴性率。结果表明，CLIP 模型学习的表示对涉及云的卫星图像处理任务具有用途。