Bitemporal supervised learning paradigm always dominates remote sensing
change detection using numerous labeled bitemporal image pairs, especially for
high spatial resolution (HSR) remote sensing imagery. However, it is very
expensive and labor-intensive to label change regions in large-scale bitemporal
HSR remote sensing image pairs. In this paper, we propose single-temporal
supervised learning (STAR) for universal remote sensing change detection from a
new perspective of exploiting changes between unpaired images as supervisory
signals. STAR enables us to train a high-accuracy change detector only using
unpaired labeled images and can generalize to real-world bitemporal image
pairs. To demonstrate the flexibility and scalability of STAR, we design a
simple yet unified change detector, termed ChangeStar2, capable of addressing
binary change detection, object change detection, and semantic change detection
in one architecture. ChangeStar2 achieves state-of-the-art performances on
eight public remote sensing change detection datasets, covering above two
supervised settings, multiple change types, multiple scenarios. The code is
available at this https URL

提出了一种新的单时序监督学习方法（STAR），用于遥感变化检测，通过利用未配对图像之间的变化来训练高准确性的变化检测器，并且适用于实际的双时序图像对。通过设计具有二进制变化检测、目标变化检测和语义变化检测能力的简单统一的 ChangeStar2 算法，STAR 的灵活性和可伸缩性得到了证明，该算法在八个公共遥感变化检测数据集上取得了最先进的性能。