Ten Years of Patch-Based Approaches for Sar Imaging: A Review

Tupin, Florence; Dénis, Loïc; Deledalle, Charles-Alban; Ferraioli, Giampaolo

doi:10.1109/igarss.2019.8900596

Cited by 6 publications

(2 citation statements)

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“…have gained high popularity due to their ability to preserve spatial features while not sacrificing image resolution [142]. Deledalle et al [143] proposed one of the first nonlocal patchbased methods applied to speckle reduction by taking into account the statistical properties of speckle combined with the original nonlocal image denoising algorithm introduced in [144].…”

Section: Despecklingmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

215

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

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Section: Despecklingmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

215

View full text Add to dashboard Cite

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“…The large success of patch-based methods in image denoising [15], [16] fueled a large body of research [17], [18], from intensity-image restoration [19]- [21] to interferometric [22], polarimetric [23], [24] or polarimetric and interferometric [25] modalities.…”

Section: Introductionmentioning

confidence: 99%

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

Dalsasso,

Denis,

Tupin

2021

Preprint

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This article is currently under review in IEEE Transactions on Geoscience and Remote Sensing. 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 hasslefree 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.

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