SAR2SAR: a semi-supervised despeckling algorithm for SAR images

Abstract

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal 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 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.

arXiv commentThe manuscript is the accepted version of IEEE STARS. Code is made available at https://gitlab.telecom-paris.fr/RING/SAR2SAR

References in corpus (5)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Noise2Noise: Learning Image Restoration without Clean Data
• SAR Image Despeckling Using a Convolutional Neural Network
• MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?
• SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy

Cited by in corpus (6)

• Image Restoration for Remote Sensing: Overview and Toolbox
• As if by magic: self-supervised training of deep despeckling networks with MERLIN
• Multi-temporal speckle reduction with self-supervised deep neural networks
• A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
• Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
• Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning