Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images

• URL: http://arxiv.org/abs/2207.11111v1
• Date: Fri, 22 Jul 2022 14:38:37 GMT
• Title: Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
• Authors: In\`es Meraoumia, Emanuele Dalsasso, Lo\"ic Denis, Florence Tupin
• Abstract summary: This paper explores two fast strategies employing a single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal framework. The first one is based on Quegan filter 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"
• Score: 3.5136650608171833
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal framework. The first one is based on Quegan filter 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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