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 …
A review of remote sensing image fusion methods
H Ghassemian - Information Fusion, 2016 - Elsevier
The recent years have been marked by continuous improvements of remote sensors with
applications like monitoring and management of the environment, precision agriculture …
applications like monitoring and management of the environment, precision agriculture …
Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long short …
YB Özçelik, A Altan - Fractal and Fractional, 2023 - mdpi.com
Diabetic retinopathy (DR), which is seen in approximately one-third of diabetes patients
worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated …
worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated …
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
Lung abnormality is one of the common diseases in humans of all age group and this
disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 …
disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 …
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 …
Highly accurate energy consumption forecasting model based on parallel LSTM neural networks
The main challenges of the energy consumption forecasting problem are the concerns for
reliability, stability, efficiency and accuracy of the forecasting methods. The existing …
reliability, stability, efficiency and accuracy of the forecasting methods. The existing …
A critical comparison among pansharpening algorithms
G Vivone, L Alparone, J Chanussot… - … on Geoscience and …, 2014 - ieeexplore.ieee.org
Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result
of the processing with the spectral resolution of the former and the spatial resolution of the …
of the processing with the spectral resolution of the former and the spatial resolution of the …
A hybrid LSTM neural network for energy consumption forecasting of individual households
Irregular human behaviors and univariate datasets remain as two main obstacles of data-
driven energy consumption predictions for individual households. In this study, a hybrid …
driven energy consumption predictions for individual households. In this study, a hybrid …
Hyperspectral pansharpening: A review
L Loncan, LB De Almeida… - … and remote sensing …, 2015 - ieeexplore.ieee.org
Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate
an image with the high spatial resolution of the former and the high spectral resolution of the …
an image with the high spatial resolution of the former and the high spectral resolution of the …
Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges
In this paper, the development of pansharpening methods from traditional understanding to
the current understanding is comprehensively reviewed. Furthermore, the performance of …
the current understanding is comprehensively reviewed. Furthermore, the performance of …