Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …

Convolutional neural networks (CNNs) and Transformers have made impressive progress in
the field of remote sensing change detection (CD). However, both architectures have …

In remote sensing (RS), multiple modalities of data are usually available, eg, RGB,
multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture …

Change detection is a critical task in studying the dynamics of ecosystems and human
activities using multi-temporal remote sensing images. While deep learning has shown …

Change detection on multimodal remote sensing images has become an increasingly
interesting and challenging topic in the remote sensing community, which can play an …

The rapid advancement of automated artificial intelligence algorithms and remote sensing
instruments has benefited change detection (CD) tasks. However, there is still a lot of space …

With recent advances in hyperspectral remote sensing, hyperspectral change detection
(HCD) methods have been developed for precision agriculture and land cover/use …

Multimodal change detection (CD) is an increasingly interesting yet highly challenging
subject in remote sensing. To facilitate the comparison of multimodal images, some image …

Self-supervised learning guided by masked image modeling, such as masked autoencoder
(MAE), has attracted wide attention for pretraining vision transformers in remote sensing …

Heterogeneous change detection (HCD) is a process to determine the change information
by analyzing heterogeneous images of the same geographic location taken at different …