  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 https://github.com/Z-Zheng/pytorch-change-models.
