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Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning

Abstract : Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. The wide availability of multi-temporal stacks of SAR images can improve even further the quality of denoising. In this paper, we propose a flexible yet efficient way to integrate temporal information into a deep neural network for speckle suppression. Archives provide access to long time-series of SAR images, from which multi-temporal averages can be computed with virtually no remaining speckle fluctuations. The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image. This simple strategy is shown to offer a noticeable improvement compared to filtering the original image without knowledge of the multi-temporal average.
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https://hal.telecom-paris.fr/hal-03129020
Contributor : Emanuele Dalsasso Connect in order to contact the contributor
Submitted on : Tuesday, February 2, 2021 - 3:54:17 PM
Last modification on : Tuesday, September 21, 2021 - 2:16:04 PM

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  • HAL Id : hal-03129020, version 1
  • ARXIV : 2102.00682

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Emanuele Dalsasso, Inès Meraoumia, Loïc Denis, Florence Tupin. Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning. IGARSS 2021, Jul 2021, Bruxelles (virtual), Belgium. ⟨hal-03129020⟩

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