Change detection has been increasingly studied in remote and close-range sensing in the
last decades, driven by its importance in environment monitoring and database updating …

The bi-temporal change detection (CD) is still challenging for high-resolution optical remote
sensing data analysis due to various factors such as complex textures, seasonal variations …

Remote sensing images pose distinct challenges for downstream tasks due to their inherent
complexity. While a considerable amount of research has been dedicated to remote sensing …

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 …

Change detection (CD) is a critical task to observe and analyze dynamic processes of land
cover. Although numerous deep-learning (DL)-based CD models have performed …

Despite the success of deep learning-based change detection (CD) methods, their existing
insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them …

Self-supervised learning (SSL) has shown remarkable success in image representation
learning. Among these methods, masked image modeling and contrastive learning are the …

Change detection is widely used in the field of building monitoring. In recent years, the
progress of remote sensing image technology has provided high-resolution data. However …

Most contemporary supervised remote sensing (RS) image change detection (CD)
approaches are customized for equal-resolution bitemporal images. Real-world applications …

Current transformer-based change detection (CD) approaches either employ a pretrained
model trained on a large-scale image classification ImageNet dataset or rely on first …