Change Detection With Cross-Domain Remote Sensing Images: A Systematic Review

Change detection (CD) is one of the most important research areas in remote sensing. With the fast development of imaging techniques, CD using cross-domain remote sensing images (CDCD) has attracted extensive attention from the community. However, CDCD is quite challenging because of the different properties of the cross-domain remote sensing images (collected from different sensors or under different imaging conditions), which triggers difference in physical quantity, noise effect, illumination, seasonal conditions, geometric, and visual appearance. In the past decades, although many attempts have been devoted to the above challenges, a review of CDCD is still lacking. To bridge this gap, this article provides a systematic review on CDCD, with emphasis on image preprocessing (i.e., geometric registration et al.), feature representation (i.e., conventional methods and deep learning (DL)-based methods), and change detectors (i.e., similarity-based detector and joint feature-loss detector). Moreover, extensive experiments have also been conducted to compare the performance of 17 widely utilized CDCD methods. Based on this comparison, directions for future developments of CDCD, which include large-scale CDCD datasets, foundation models, and specialized models, are also discussed.