The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover
Change Detection (LCCD) has gained increasing significance in scientific discovery and …

Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring,
territorial planning, and disaster assessment. With the abundance of data collected by …

Samples play a crucial role in the supervised classification of remote-sensing images.
However, labeling large samples for training a classifier or deep-learning network is not only …

With the rapid development of remote sensing data fusion technology, heterogeneous
remote sensing image (HRSI) change detection (CD) has become a frontier field. The …

Hyperspectral change detection, which provides abundant information on land cover
changes in the Earth's surface, has become one of the most crucial tasks in remote sensing …

Remote sensing change detection (RSCD), which aims to identify surface changes from
bitemporal images, is significant for many applications, such as environmental protection …

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 …

There are various binary semantic segmentation tasks in remote sensing (RS) that aim to
extract the foreground areas of interest, such as buildings and roads, from the background in …

With the rapid development of society, the pace of land change continues to accelerate.
Consequently, remote sensing change detection (CD) has become a vital method for …

Supervised classification with hyperspectral remote-sensing images (HRSIs) plays an
important role in practical applications. However, labeling samples with HRSIs for …