Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …

Deep learning (DL) approaches have been extensively applied to change detection in
hyperspectral images (HSIs). However, the majority of them encounter scarcity of training …

The fast development of self-supervised learning (SSL) lowers the bar learning feature
representation from massive unlabeled data and has triggered a series of researches on …

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …

Recently, deep neural networks (DNNs) have been widely used in hyperspectral image
change detection (HSI-CD). Generally, training such a DNN-based HSI-CD network often …

Change detection (CD) is a hot topic and has been applied in many fields. Very high
resolution (VHR) images contain the rich spatial information, and are widely used in CD …

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 …

Deep learning-based methods (eg, convolutional neural network (CNN)-based methods)
have shown increasing potential in hyperspectral image (HSI) change detection (CD) …

Change detection is a process of identifying changed ground objects by comparing image
pairs obtained at different times. Compared with the pixel-level and object-level change …