384 citations · 769 across the 10 of their papers we have counts for
10 papers
Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
Giuseppe Guarino, Matteo Ciotola, Gemine Vivone +2
Hyperspectral pansharpening has received much attention in recent years due to technological and methodological advances that open the door to new application scenarios. However, r…
Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives
Matteo Ciotola, Giuseppe Guarino, Gemine Vivone +4
Hyperspectral pansharpening consists of fusing a high-resolution panchromatic band and a low-resolution hyperspectral image to obtain a new image with high resolution in both the s…
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
Giuseppe Guarino, Matteo Ciotola, Gemine Vivone +1
Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges. It consists in a pixel-level…
A full-resolution training framework for Sentinel-2 image fusion
Matteo Ciotola, Mario Ragosta, Giovanni Poggi +1
This work presents a new unsupervised framework for training deep learning models for super-resolution of Sentinel-2 images by fusion of its 10-m and 20-m bands. The proposed schem…
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
Matteo Ciotola, Giovanni Poggi, Giuseppe Scarpa
In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images. Given the lack of ground truth data, most deep learning-based methods carry…
Pansharpening by convolutional neural networks in the full resolution framework
Matteo Ciotola, Sergio Vitale, Antonio Mazza +2
In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an e…
Full-resolution quality assessment for pansharpening
Giuseppe Scarpa, Matteo Ciotola
A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground-truths t…
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro, Enrico Magli, Giovanni Poggi +3
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Desp…
Guided patch-wise nonlocal SAR despeckling
Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa +2
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, o…
Target-adaptive CNN-based pansharpening
Giuseppe Scarpa, Sergio Vitale, Davide Cozzolino
We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper,…