Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …

Earth observation (EO) satellite missions have been providing detailed images about the
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

Satellite imaging generally presents a trade-off between the frequency of acquisitions and
the spatial resolution of the images. Super-resolution is often advanced as a way to get the …

Multispectral Sentinel-2 (S-2) images are a valuable source of Earth observation data;
however, spatial resolution of their spectral bands limited to 10-, 20-, and 60-m ground …

Insufficient image spatial resolution is a serious limitation in many practical scenarios,
especially when acquiring images at a finer scale is infeasible or brings higher costs. This is …

Real-world degradations deviate from ideal degradations, as most deep learning-based
scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly …

Remote sensing images exhibit rich texture features and strong autocorrelation. Although
the super-resolution (SR) method of remote sensing images based on convolutional neural …

Remote sensing super-resolution (SR) technique, which aims to generate high-resolution
image with rich spatial details from its low-resolution counterpart, play a vital role in many …

Single-image super-resolution (SISR) for high-resolution (HR) remote sensing image (RSI)
acquisition is becoming increasingly valuable and important, and convolutional neural …