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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 …
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 …
TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution
Transformer-based method has demonstrated promising performance in image super-
resolution tasks, due to its long-range and global aggregation capability. However, the …
resolution tasks, due to its long-range and global aggregation capability. However, the …
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 …
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 …
Hierarchical slice interaction and multi-layer cooperative decoding networks for remote sensing image dehazing
M Yu, SY Xu, H Sun, YL Zheng, W Yang - Image and Vision Computing, 2024 - Elsevier
Recently, U-shaped neural networks have gained widespread application in remote sensing
image dehazing and achieved promising performance. However, most of the existing U …
image dehazing and achieved promising performance. However, most of the existing U …
A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example
R Liu, B Cui, W Dong, X Fang, Y **ao, X Zhao… - Journal of Hazardous …, 2024 - Elsevier
Harmful algal blooms (HABs) are challenging to recognize because of their striped and
uneven biomass distributions. To address this issue, a refined deep-learning algorithm …
uneven biomass distributions. To address this issue, a refined deep-learning algorithm …
Generating a long-term (2003− 2020) hourly 0.25° global PM2. 5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)
Generating a long-term high-spatiotemporal resolution global PM 2.5 dataset is of great
significance for environmental management to mitigate the air pollution concerns worldwide …
significance for environmental management to mitigate the air pollution concerns worldwide …
A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution
Remote sensing images exhibit rich texture features and strong autocorrelation. Although
the super-resolution (SR) method of remote sensing images based on convolutional neural …
the super-resolution (SR) method of remote sensing images based on convolutional neural …
[HTML][HTML] An adaptive multi-perceptual implicit sampling for hyperspectral and multispectral remote sensing image fusion
C Zhu, R Dai, L Gong, L Gao, N Ta, Q Wu - International Journal of Applied …, 2023 - Elsevier
Hyperspectral and multispectral remote sensing image fusion (HMIF) can effectively
enhance image spatial-spectral information representation. However, existing deep learning …
enhance image spatial-spectral information representation. However, existing deep learning …