SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images
TLDR
A deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR, where Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions.Abstract:
Speckle reduction is a key step in many remote sensing applications By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyze Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions To this purpose, the recently proposed noise2noise framework [1] has been employed The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters Then, results on real images are discussed, to show the potential of the proposed algorithm The code is made available to allow testing and reproducible research in this fieldread more
Citations
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Journal ArticleDOI
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro,Enrico Magli,Giovanni Poggi,Giuseppe Scarpa,Diego Valsesia,Luisa Verdoliva +5 more
TL;DR: In this paper, a survey of deep learning methods applied to SAR despeckling is presented, with the objective of identifying the most promising lines and identifying the factors that have limited the success of deep models.
Journal ArticleDOI
Image Restoration for Remote Sensing: Overview and toolbox
TL;DR: In this paper , the authors proposed a method to recover a true unknown image from a degraded observed one by restoring the original image using a set of noise types and artifacts from the observed image.
Journal ArticleDOI
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro,Enrico Magli,Giovanni Poggi,Giuseppe Scarpa,Diego Valsesia,Luisa Verdoliva +5 more
TL;DR: In this article, the authors provide a critical analysis of existing methods with the objective to recognize the most promising lines, identify the factors that have limited the success of deep learning for SAR despeckling, and propose ways forward in an attempt to fully exploit the potential of DNN for SAR.
Journal ArticleDOI
Image denoising and despeckling methods for SAR images to improve image enhancement performance: a survey
E Ponmani,P Saravanan +1 more
TL;DR: The basics of SAR imaging, steps in the pipeline of SAR despeckling process, filters like Lee filter, Frost filter, Kuan Filter and Gamma Maximum a posteriori (MAP) filter, various state of the art despekling methods and deep learning approaches for SAR desPeckling are discussed.
Posted ContentDOI
As if by magic: self-supervised training of deep despeckling networks with MERLIN
TL;DR: In this paper, a self-supervised strategy called MERLIN (coMplex sElf-supeRvised despeckLINg) is proposed to separate real and imaginary parts of single-look complex SAR images.
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