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

• URL: http://arxiv.org/abs/2102.00682v1
• Date: Mon, 1 Feb 2021 07:48:55 GMT
• Title: Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
• Authors: Emanuele Dalsasso, In\`es Meraoumia, Lo\"ic Denis, Florence Tupin
• Abstract summary: Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. We propose a flexible yet efficient way to integrate temporal information into a deep neural network for speckle suppression.
• Score: 3.506018346865459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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