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 …

Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial
task for identifying the change areas on the Earth's surface. However, the utilization of …

Self-supervised contrastive learning (CL) can learn high-quality feature representations that
are beneficial to downstream tasks without labeled data. However, most CL methods are for …

Hyperspectral image change detection (HSI-CD) is a technique to accurately detect land
cover changes by using HSIs with rich spatial–spectral information. In recent years, the HSI …

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water
bodies from the rich spatial and spectral information contained in the images. It is a time and …

Change detection (CD) in remote sensing (RS) images is a critical task that has achieved
significant success by deep learning. Current networks often employ pixel-based …

Discriminant features captured from hyperspectral images (HSIs) can be used to accurately
distinguish on-ground objects and materials for Earth observation. Typically, this process is …

Deep learning-based hyperspectral image (HSI) change detection (CD) has been a
research hotspot. However, the high dimensionality and the limited training samples make …

Deep-Learning-based (DL-based) approaches have achieved remarkable performance in
hyperspectral image (HSI) change detection (CD). Convolutional Neural Networks (CNNs) …

Computational intelligence techniques have been widely used for automatic building
detection from high-resolution remote sensing imagery and especially the methods based …