Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …

Current state-of-the-art remote sensing salient object detectors always require high-
resolution spatial context to ensure excellent performance, which incurs enormous …

Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …

Super-resolution (SR) techniques play a crucial role in increasing the spatial resolution of
remote sensing data and overcoming the physical limitations of the spaceborne imaging …

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

The low resolution of remote sensing images often limits the land cover classification (LCC)
performance. Super resolution (SR) can improve the image resolution, while greatly …

The production of highly accurate land cover maps is one of the primary challenges in
remote sensing, which depends on the spatial resolution of the input images. Sometimes …

The increased frequency of forest fires in recent years has raised concerns about the high
cost associated with traditional forest fire prevention methods. To address this issue, this …

Super-resolution (SR) methods have significantly contributed to the improvement of the
spatial resolution of remote sensing (RS) images. The development of deep learning …