无需标注数据，通过时间顺序恢复自动发现卫星图像中的长期变化
OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data
- 利用时间序列顺序重建任务实现自监督学习
- 在变化检测上AUROC达87.6%，较基线提升31.3个百分点
- 适合遥感监测、环境变化分析等无标签场景
21世纪面临严峻的环境问题，监测地球表面变化至关重要。大规模遥感，如卫星影像，是实现该目标的重要工具。然而，由于缺乏带变化标签的卫星数据，尤其是罕见变化类别，使用有监督方法检测变化存在困难。标注困难源于变化在影像中出现稀疏，即使在大量图像中，真正发生持久变化的也仅占小部分。为此，我们提出OPTIMUS，一种基于直观原理的自监督学习方法：若模型能恢复时间序列中图像的相对顺序，则说明图像间存在长期变化。OPTIMUS通过在时间序列模型输出上应用变化点检测来验证该原理。实验表明，OPTIMUS可直接检测出卫星影像中的显著变化，在区分变化与未变化时间序列方面，AUROC得分从56.3%提升至87.6%，显著优于基线。代码与数据集已公开于https://huggingface.co/datasets/optimus-change/optimus-dataset/。
原文摘要 · Abstract (English)
In the face of pressing environmental issues in the 21st century, monitoring surface changes on Earth is more important than ever. Large-scale remote sensing, such as satellite imagery, is an important tool for this task. However, using supervised methods to detect changes is difficult because of the lack of satellite data annotated with change labels, especially for rare categories of change. Annotation proves challenging due to the sparse occurrence of changes in satellite images. Even within a vast collection of images, only a small fraction may exhibit persistent changes of interest. To address this challenge, we introduce OPTIMUS, a self-supervised learning method based on an intuitive principle: if a model can recover information about the relative order of images in the time series, then that implies that there are long-lasting changes in the images. OPTIMUS demonstrates this principle by using change point detection methods on model outputs in a time series. We demonstrate that OPTIMUS can directly detect interesting changes in satellite images, achieving an improvement in AUROC score from 56.3% to 87.6% at distinguishing changed time series from unchanged ones compared to baselines. Our code and dataset are available at https://huggingface.co/datasets/optimus-change/optimus-dataset/.
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