[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Image fusion meets deep learning: A survey and perspective
Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …
from different source images, aims to generate a single image that is more informative and …
[HTML][HTML] Deep learning in remote sensing applications: A meta-analysis and review
Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …
image analysis over the past few years. In this study, the major DL concepts pertinent to …
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 …
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 …
Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
Zero-shot hyperspectral sharpening
Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
Model-guided deep hyperspectral image super-resolution
The trade-off between spatial and spectral resolution is one of the fundamental issues in
hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution …
hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution …
MHF-Net: An interpretable deep network for multispectral and hyperspectral image fusion
Multispectral and hyperspectral image fusion (MS/HS fusion) aims to fuse a high-resolution
multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high …
multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high …