SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images
TL
TL;DR
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 field.
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SAR Image Despeckling Employing a Recursive Deep CNN Prior
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SAR Image Despeckling by Noisy Reference-Based Deep Learning Method
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Speckle2Void: Deep Self-Supervised SAR Despeckling With Blind-Spot Convolutional Neural Networks
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SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
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