Spectral super-resolution meets deep learning: Achievements and challenges
Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …
from only RGB images, which can effectively overcome the high acquisition cost and low …
From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …
remote sensing area, and numerous methods have made remarkable progress in this …
Local-global temporal difference learning for satellite video super-resolution
Optical-flow-based and kernel-based approaches have been extensively explored for
temporal compensation in satellite Video Super-Resolution (VSR). However, these …
temporal compensation in satellite Video Super-Resolution (VSR). However, these …
Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review
Y Sun, K Deng, K Ren, J Liu, C Deng, Y Jin - ISPRS Journal of …, 2024 - Elsevier
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 …
weather forecasting, climate change, and agriculture. The regional and local studies call for …
EDiffSR: An efficient diffusion probabilistic model for remote sensing image super-resolution
Recently, convolutional networks have achieved remarkable development in remote
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
Deep blind super-resolution for satellite video
Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …
Impacts of urbanization on heat in Ho Chi Minh, southern Vietnam using U-Net model and remote sensing
ANT Do, HD Tran, TAT Do - International Journal of Environmental …, 2024 - Springer
Green space in cities has been reducing rapidly due to the intensive urban expansion,
which contributes to surface temperature growth, leading to numerous challenges in …
which contributes to surface temperature growth, leading to numerous challenges in …
[HTML][HTML] A synchronized estimation of hourly surface concentrations of six criteria air pollutants with GEMS data
Abstract Machine learning is widely used to infer ground-level concentrations of air
pollutants from satellite observations. However, a single pollutant is commonly targeted in …
pollutants from satellite observations. However, a single pollutant is commonly targeted in …
A study on identifying synergistic prevention and control regions for PM2. 5 and O3 and exploring their spatiotemporal dynamic in China
Air pollutants, notably ozone (O 3) and fine particulate matter (PM 2.5) give rise to evident
adverse impacts on public health and the ecotope, prompting extensive global …
adverse impacts on public health and the ecotope, prompting extensive global …
Global attention‐enabled texture enhancement network for MR image reconstruction
Y Li, J Yang, T Yu, J Chi, F Liu - Magnetic Resonance in …, 2023 - Wiley Online Library
Purpose Although recent convolutional neural network (CNN) methodologies have shown
promising results in fast MR imaging, there is still a desire to explore how they can be used …
promising results in fast MR imaging, there is still a desire to explore how they can be used …