SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR. Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework [1] has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field.

Automated summary

This article introduces a semi-supervised deep learning algorithm SAR2SAR for speckle reduction, utilizing multitemporal time series and adapting to the speckle statistics to restore SAR images. The recently proposed noise2noise framework is employed for this purpose, with a performance comparison study using synthetic speckle noise, and the potential of the algorithm is demonstrated on real images.