A new benchmark based on recent advances in multispectral pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods
Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN)
data aimed at generating an outcome with the same spatial resolution of the PAN data and …
data aimed at generating an outcome with the same spatial resolution of the PAN data and …
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
AY Sun, BR Scanlon - Environmental Research Letters, 2019 - iopscience.iop.org
Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …
of environment and water management (EWM). Big Data are information assets …
Multispectral and hyperspectral image fusion in remote sensing: A survey
G Vivone - Information Fusion, 2023 - Elsevier
The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the
spotlight. The combination of high spatial resolution MS images with HS data showing a …
spotlight. The combination of high spatial resolution MS images with HS data showing a …
Machine learning in pansharpening: A benchmark, from shallow to deep networks
Machine learning (ML) is influencing the literature in several research fields, often through
state-of-the-art approaches. In the past several years, ML has been explored for …
state-of-the-art approaches. In the past several years, ML has been explored for …
Detail injection-based deep convolutional neural networks for pansharpening
The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired
multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called …
multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
GuidedNet: A general CNN fusion framework via high-resolution guidance for hyperspectral image super-resolution
Hyperspectral image super-resolution (HISR) is about fusing a low-resolution hyperspectral
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …
ESSAformer: Efficient transformer for hyperspectral image super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks
Hyperspectral images (HSIs) are of crucial importance in order to better understand features
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
PanNet: A deep network architecture for pan-sharpening
We propose a deep network architecture for the pan-sharpening problem called PanNet. We
incorporate domain-specific knowledge to design our PanNet architecture by focusing on …
incorporate domain-specific knowledge to design our PanNet architecture by focusing on …