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 …

Deep Learning (DL) methods have gained significant recognition in hydrology and water
resources applications in recent years. Beginning with a discussion on fundamental …

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 …

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 …

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 …

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 …

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 …

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

Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …

A simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed
images. Following an initial random allocation of “soft” pixel proportions to “hard” sub-pixel …