In remote sensing, change detection has always been a fundamental yet challenging research topic, with profound theoretical significance and extensive application value. Over the past decades, the emergence and development of deep learning has provided new technical supports for supervised change detection and advanced its accuracy to unprecedented levels. Nevertheless, owing to the strong reliance and weak transferability of pre-labeled references, supervised learning modes still require some degrees of human assistance, which is not applicable to all the change detection tasks. In addition, ...
