Recently, deep learning (DL) models have become the main focus for the remote sensing
change detection tasks. Numerous publications on supervised and unsupervised DL-based …

Existing optical remote sensing image change detection (CD) methods aim to learn an
appropriate discriminate decision by analyzing the feature information of bitemporal images …

Change detection in urban areas can be helpful for urban resource management and smart
city planning. The effects of human activities on the environment and ground have gained …

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

Concerning the ever-changing wetland environment, the efficient extraction of wetland
information holds great significance for the research and management of wetland …

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 …

High-precision land cover maps of remote sensing images based on an intelligent extraction
method are an important research field for many scholars. In recent years, deep learning …

As essential components of human society, buildings serve a multitude of functions and
significance. Convolutional Neural Network (CNN) has made remarkable progress in the …

Remote sensing image change detection (RSI-CD) aims to explore surface change
information from aligned dual-phase images. However, RSI-CD currently encounters two …

Deep-learning techniques have made significant advances in remote sensing change
detection task. However, it remains a great challenge to detect the details of changed areas …