Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

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 …

Despite its fruitful applications in remote sensing, image super-resolution (SR) is
troublesome to train and deploy as it handles different resolution magnifications with …

Recently, optical remote-sensing images have been widely applied in fields such as
environmental monitoring and land cover classification. However, due to limitations in …

The deep learning based super-resolution (SR) methods have recently achieved
remarkable progress in the reconstruction of ideally simulated high-quality remote sensing …

Most of the Greenland and Antarctic ice sheets are covered with firn—the transitional
material between snow and glacial ice. Firn is vital for understanding ice-sheet mass …

Synthetic aperture radar (SAR) images acquired by airborne sensors or remote sensing
satellites contain the necessary information that can be used to investigate various objects of …

Mangroves are an essential component of coastal ecosystems. Accurate and effective
identification and extraction of mangrove areas from remote sensing imagery is crucial for …

Remote sensing (RS) technology has significantly contributed to geological exploration and
mineral resource assessment. However, its effective application in vegetated areas …

Using super-resolution (SR) algorithms, an image with a low resolution can be converted
into a high-quality image. Our objective was to compare deep learning-based SR models to …