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

翻译：对于高空间分辨率(HSR)遥感图像而言,使用许多贴有标签的时装图像,时装监督学习总是在变化检测中占主导地位。然而,将大型时装HSR遥感图像贴上对齐标签非常昂贵和费时。在本文中,我们提议单时监控学习(STAR),从新角度将未受定位图像的天体变化作为监督信号来探测变化。STAR使我们能够培训高准确度变化检测器,但只能使用\ textbf{unpaid}标签图像,并普遍用于真实世界的时装图像。为了评估STAR的有效性,我们设计了一个简单而有效的变化检测器,称为变异星,可以再利用任何变异Mixin模块的深层语系结构。综合实验结果表明,变星在单一时装监控下大大超越基线,并在咬口服监管下实现优异性性。代码可在https://github.com/Z-Zheng/Change Star查阅。