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FAST STRATEGIES FOR MULTI-TEMPORAL SPECKLE REDUCTION OF SENTINEL-1 GRD IMAGES

Abstract : Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a singleimage despeckling algorithm, namely SAR2SAR [1], in a multi-temporal framework. The first one is based on Quegan filter [2] and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a "super-image", i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost.
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https://hal.telecom-paris.fr/hal-03756068
Contributor : Florence Tupin Connect in order to contact the contributor
Submitted on : Monday, August 22, 2022 - 11:22:12 AM
Last modification on : Monday, August 29, 2022 - 9:59:48 AM
Long-term archiving on: : Wednesday, November 23, 2022 - 8:15:59 PM

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  • HAL Id : hal-03756068, version 1

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Inès Meraoumia, Emanuele Dalsasso, Loïc Denis, Florence Tupin. FAST STRATEGIES FOR MULTI-TEMPORAL SPECKLE REDUCTION OF SENTINEL-1 GRD IMAGES. IGARSS, 2022, Kuala Lumpur, Malaysia. ⟨hal-03756068⟩

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