Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Vegetation cover change is one of the key indicators used for monitoring environmental
quality. It can accurately reflect changes in hydrology, climate, and human activities …

High-resolution images have a wide range of applications in image compression, remote
sensing, medical imaging, public safety, and other fields. The primary objective of super …

Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely
sensed data. Although significant progress has been made in image classification based …

Spatial resolution is a crucial indicator for measuring the quality of hyperspectral imaging
(HSI) and obtaining high-resolution (HR) hyperspectral images without any auxiliary …

Generative adversarial network (GAN) has made great progress in recent natural image
super-resolution tasks. The key to its success is the integration of a discriminator which is …

Easy and efficient acquisition of high-resolution remote sensing images is of importance in
geographic information systems. Previously, deep neural networks composed of …

Image super-resolution (SR) methods can generate remote sensing images with high spatial
resolution without increasing the cost of acquisition equipment, thereby providing a feasible …

Downscaling has an important role to play in remote sensing. It allows prediction at a finer
spatial resolution than that of the input imagery, based on either (i) assumptions or prior …