Change detection using deep learning methods in optical remote sensing imageries has become the mainstream method, primarily due to their strong spatial-temporal transferability. However, current change detection models are designed for high-quality optical remote sensing imageries and seldom account for image conditions such as haze cover or low-light scenarios. As we know, the quality of optical remote sensing imageries can be severely degraded by haze or low-light conditions, which in turn significantly impairs the performance of change detection models. Yet there is still a lack of advance...
