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Found 9 papers, 6 papers with code
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges.
A full-resolution training framework for Sentinel-2 image fusion
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.
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images.
Ranked #1 on
Pansharpening
on GeoEye-1 PairMax
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework
In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss.
Ranked #2 on
Pansharpening
on WorldView-3 Adelaide
Pansharpening by convolutional neural networks in the full resolution framework
A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images.
Full-resolution quality assessment for pansharpening
Both reference-based and no-reference indexes present critical shortcomings which motivate the community to explore new solutions.
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
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.
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art.
