  For high spatial resolution (HSR) remote sensing images, bitemporal
supervised learning always dominates change detection using many pairwise
labeled bitemporal images. However, it is very expensive and time-consuming to
pairwise label large-scale bitemporal HSR remote sensing images. In this paper,
we propose single-temporal supervised learning (STAR) for change detection from
a new perspective of exploiting object changes in unpaired images as
supervisory signals. STAR enables us to train a high-accuracy change detector
only using \textbf{unpaired} labeled images and generalize to real-world
bitemporal images. To evaluate the effectiveness of STAR, we design a simple
yet effective change detector called ChangeStar, which can reuse any deep
semantic segmentation architecture by the ChangeMixin module. The comprehensive
experimental results show that ChangeStar outperforms the baseline with a large
margin under single-temporal supervision and achieves superior performance
under bitemporal supervision. Code is available at
https://github.com/Z-Zheng/ChangeStar
