An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations. However, they ignore the universal domain shift induced by time-varying land cover changes, including luminance fluctuations and seasonal changes between pre-event and post-event images, thereby producing suboptimal results.