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 …
A self-supervised remote sensing image fusion framework with dual-stage self-learning and spectral super-resolution injection
Pan-sharpening is a very productive technique to enhance the spatial details of multispectral
images with the aid of panchromatic images. Nowadays, deep learning-based pan …
images with the aid of panchromatic images. Nowadays, deep learning-based pan …
[HTML][HTML] Geospatial applicability optics of the TROPOspheric monitoring instrument (TROPOMI) on a global scale: An overview
A Neckel, E Goellner, MLS Oliveira, PC Toscan… - Geoscience …, 2025 - Elsevier
Studies arising from literature reviews are important as they facilitate specific understanding
about the use of the Sentinel-5P satellite developed by the European Space Agency (ESA) …
about the use of the Sentinel-5P satellite developed by the European Space Agency (ESA) …
Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO …
B Chen, J Hu, Y Wang - npj Climate and Atmospheric Science, 2024 - nature.com
Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal
resolution is crucial for assessing its meteorological-environmental-health impacts. Although …
resolution is crucial for assessing its meteorological-environmental-health impacts. Although …
Remote sensing image super-resolution via cross-scale hierarchical transformer
Global and local modeling is essential for image super-resolution tasks. However, current
efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth …
efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth …
-APNet: A multimodal deep learning network to predict major air pollutants from temporal satellite images
Air quality monitoring plays a vital role in the sustainable development of any country.
Continuous monitoring of the major air pollutants and forecasting their variations would be …
Continuous monitoring of the major air pollutants and forecasting their variations would be …
Local and regional enhancements of CH, CO, and CO inferred from TCCON column measurements
K Mottungan, C Roychoudhury… - Atmospheric …, 2024 - amt.copernicus.org
In this study, we demonstrate the utility of available correlative measurements of carbon
species to identify regional and local air mass characteristics as well as their associated …
species to identify regional and local air mass characteristics as well as their associated …
Long-term spatial and temporal evaluation of the PM2. 5 and PM10 mass concentrations in Lithuania
M Davtalab, S Byčenkienė, V Bimbaitė - Atmospheric Pollution Research, 2023 - Elsevier
Evaluating the spatio-temporal trends of particulate mass concentration is beneficial for
appraising the risk to human health and environmental quality. In this study, the long-term …
appraising the risk to human health and environmental quality. In this study, the long-term …