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

TL;DR

This paper introduces a deep learning method that leverages multi-temporal SAR image stacks to enhance speckle reduction, significantly improving denoising quality by integrating temporal averages with neural networks.

Contribution

It proposes a novel approach combining multi-temporal averages and ratio images within a deep neural network for superior speckle suppression in SAR images.

Findings

Noticeable improvement over traditional filtering methods

Effective integration of multi-temporal information enhances denoising

Simple ratio-based strategy improves speckle reduction quality

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.