IEEE Transactions on Pattern Analysis and Machine Intelligence

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

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

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. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.

Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives

Remote sensing provides valuable information about objects and areas from a distance in either active (e.g., radar and lidar) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering a true unknown image from a degraded observed one. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques along different paths according to sensor type. 

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Image Restoration for Remote Sensing: Overview and toolbox

Speckle fluctuations seriously limit the interpretability of synthetic
aperture radar (SAR) images. Speckle reduction has thus been the subject of
numerous works spanning at least four decades. Techniques based on deep neural
networks have recently achieved a new level of performance in terms of SAR
image restoration quality. Beyond the design of suitable network architectures
or the selection of adequate loss functions, the construction of training sets
is of uttermost importance. So far, most approaches have considered a
supervised training strategy: the networks are trained to produce outputs as
close as possible to speckle-free reference images. Speckle-free images are
generally not available, which requires resorting to natural or optical images
or the selection of stable areas in long time series to circumvent the lack of
ground truth. Self-supervision, on the other hand, avoids the use of
speckle-free images. We introduce a self-supervised strategy based on the
separation of the real and imaginary parts of single-look complex SAR images,
called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a
straightforward way to train all kinds of deep despeckling networks. Networks
trained with MERLIN take into account the spatial correlations due to the SAR
transfer function specific to a given sensor and imaging mode. By requiring
only a single image, and possibly exploiting large archives, MERLIN opens the
door to hassle-free as well as large-scale training of despeckling networks.
The code of the trained models is made freely available at
https://gitlab.telecom-paris.fr/RING/MERLIN.

As if by magic: self-supervised training of deep despeckling networks with MERLIN

Speckle noise strongly affects Synthetic Aperture Radar (SAR) images, causing strong intensity fluctuations that make them difficult to analyze. Although many speckle reduction algorithms have been proposed, how to effectively deal with the spatial correlations of speckle remains an open question, especially in the most recent deep learning approaches. This paper tries to address this problem. Existing approaches to tackle the speckle correlations are described. Then, a standard training strategy for deep learning is proposed. Two models are trained and the increased robustness brought by including a Total Variation (TV) term in the loss function is analyzed on Sentinel-1 images.

How to handle spatial correlations in SAR despeckling? Resampling strategies and deep learning approaches

The speckle phenomenon remains a major hurdle for the analysis of SAR images. The development of speckle reduction methods closely follows methodological progress in the field of image restoration. The advent of deep neural networks has offered new ways to tackle this longstanding problem. Deep learning for speckle reduction is a very active research topic and already shows restoration performances that exceed that of the previous generations of methods based on the concepts of patches, sparsity, wavelet transform or total variation minimization. The objective of this paper is to give an overview of the most recent works and point the main research directions and current challenges of deep learning for SAR image restoration.

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Emanuele Dalsasso

Papers

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

As if by magic: self-supervised training of deep despeckling networks with MERLIN

How to handle spatial correlations in SAR despeckling? Resampling strategies and deep learning approaches

A Review of Deep-Learning Techniques for SAR Image Restoration