Understanding the temporal dynamics of Earth's surface is a mission of multi-temporal
remote sensing image analysis, significantly promoted by deep vision models with its fuel …

For change detection in remote sensing, constructing a training dataset for deep learning
models is quite difficult due to the requirements of bi-temporal supervision. To overcome this …

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-
consuming and labor-intensive to collect and annotate bitemporal samples containing …

Traditional change detection methods usually follow the image differencing, change feature
extraction, and classification framework, and their performance is limited by such simple …

For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning
always dominates change detection using many pairwise labeled bitemporal images …

Detecting change through multi-image, multi-date remote sensing is essential to developing
an understanding of global conditions. Despite recent advancements in remote sensing …

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

Monitoring changes in the Earth's surface is crucial for understanding natural processes and
human impacts, necessitating precise and comprehensive interpretation methodologies …

Change detection in remote sensing images plays an important role in observing earth
surface. Over the past few years, deep learning has been widely used in image analysis due …

Remote sensing image change detection is the key to understanding surface changes.
Although the existing change detection methods have achieved good results, some …