Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
This work addresses noise reduction in satellite imagery for remote sensing applications, but it is incremental as it builds on existing deep learning methods with a simple temporal integration strategy.
The paper tackled speckle reduction in Sentinel-1 SAR images by integrating multi-temporal information into a deep learning model, resulting in noticeable improvement compared to methods without such temporal data.
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.