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 …
Spectral super-resolution via model-guided cross-fusion network
Spectral super-resolution, which reconstructs a hyperspectral image (HSI) from a single red-
green-blue (RGB) image, has acquired more and more attention. Recently, convolution …
green-blue (RGB) image, has acquired more and more attention. Recently, convolution …
Spectral super-resolution of multispectral images using spatial–spectral residual attention network
X Zheng, W Chen, X Lu - IEEE Transactions on Geoscience …, 2021 - ieeexplore.ieee.org
The spectral super-resolution of multispectral image (MSI) refers to improving the spectral
resolution of the MSI to obtain the hyperspectral image (HSI). Most recent works are based …
resolution of the MSI to obtain the hyperspectral image (HSI). Most recent works are based …
PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images
Spectral super-resolution is a very important technique to obtain hyperspectral images from
only multispectral images, which can effectively solve the high acquisition cost and low …
only multispectral images, which can effectively solve the high acquisition cost and low …
Drcr net: Dense residual channel re-calibration network with non-local purification for spectral super resolution
Spectral super resolution (SSR) aims to reconstruct the 3D hyperspectral signal from a 2D
RGB image, which is prosperous with the proliferation of Convolutional Neural Networks …
RGB image, which is prosperous with the proliferation of Convolutional Neural Networks …
Hyperspectral image super-resolution with self-supervised spectral-spatial residual network
W Chen, X Zheng, X Lu - Remote Sensing, 2021 - mdpi.com
Recently, many convolutional networks have been built to fuse a low spatial resolution (LR)
hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to …
hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to …
Cloud detection method using CNN based on cascaded feature attention and channel attention
J Zhang, J Wu, H Wang, Y Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Cloud detection is of great significance for the subsequent analysis and application of
remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In …
remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In …
HASIC-Net: Hybrid attentional convolutional neural network with structure information consistency for spectral super-resolution of RGB images
Spectral super-resolution (SSR), referring to the recovery of a reasonable hyperspectral
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
MFormer: Taming masked transformer for unsupervised spectral reconstruction
Spectral reconstruction (SR) aims to recover the hyperspectral images (HSIs) from the
corresponding RGB images directly. Most SR studies based on supervised learning require …
corresponding RGB images directly. Most SR studies based on supervised learning require …
Diverse hyperspectral remote sensing image synthesis with diffusion models
Hyperspectral image (HSI) synthesis overcomes the limitations of imaging sensors and
enables low-cost acquisition of HSIs with high spatial resolution. Using RGB as a conditional …
enables low-cost acquisition of HSIs with high spatial resolution. Using RGB as a conditional …