Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

Satellite video cameras can provide continuous observation for a large-scale area, which is
important for many remote sensing applications. However, achieving moving object …

Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-
spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high …

Each sample in the hyperspectral remote sensing image has high-dimensional features and
contains rich spatial and spectral information, which greatly increases the difficulty of feature …

Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …

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

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 …

In the field of remote sensing, due to memory consumption and computational burden, the
single-image super-resolution (SISR) methods based on deep convolution neural networks …