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arXiv · 2102.00682

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

Emanuele Dalsasso·Inès Meraoumia·Loïc Denis·Florence Tupin
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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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Emanuele Dalsasso, Inès Meraoumia, Loïc Denis, Florence Tupin. 2021-02-01. Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning. https://doi.org/10.1109/igarss47720.2021.9554555

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