Deep learning for change detection is one of the current hot topics in the field of remote
sensing. However, most end-to-end networks are proposed for supervised change …

The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …

Early Warning Systems (EWS) for near real-time detection of deforestation are a
fundamental component of public policies focusing on the reduction in forest biomass loss …

Scene change detection identifies functional changes at the scene level. Compared with
pixel-level and object-level change detection, it can provide a higher level understanding of …

Although graph convolutional networks have found application in polarimetric synthetic
aperture radar (PolSAR) image classification tasks, the available approaches cannot …

Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very
challenging area with applicability in many problems ranging from damage assessment to …

Change detection (CD) provides information about the changes on earth's surface over a
period of time. Many algorithms have been proposed over the years for effective CD of …

Scene-level change detection (SLCD) can provide semantic change information at image
level, thus it is of great significance for monitoring land use changes. Supervised SLCD …

Change detection using SAR data has been an active topic in various applications. Because
conventional change detection identifies signal changes in single-pol radar observations …

Detecting multiple changes from remote sensing imagery is a research hotspot. Very high
resolution (VHR) images contain detailed spatial information and thus are often used in …