Unsupervised SAR Despeckling by Combining Online Speckle Generation and Unpaired Training

Abstract

Speckle suppression is a crucial preliminary step for synthetic aperture radar (SAR) image processing. Supervised despeckling approaches trained on synthetic datasets usually perform poorly in practice due to the unavailability of clean SAR images. Besides, the spatial correlation of speckle is rarely considered in many methods based on the fully developed speckle assumption. In this article, we propose an unsupervised despeckling method to address these issues by combining online speckle generation and unpaired training. The method consists of two branches: the stop-gradient branch and the unpaired branch. First, the stop-gradient branch learns to generate the spatially correlated speckle. Then, the unpaired branch combines the generated speckle with the unpaired optical image to form pairs of training data for network parameter updates. More specifically, in order to generate the more realistic speckle in the stop-gradient branch, we design a speckle correction module with three SAR speckle priors: the threshold prior, the unit mean prior, and the correlation prior coupled with the weighted patch-shuffle. In the unpaired training, a hybrid loss function is employed, which takes spatial smoothness and detail protection into consideration. Afterward, we combine the stop-gradient branch with the unpaired branch by the Siamese network to achieve alternate optimization of speckle generation and speckle removal. Finally, the optimization process in our method is analyzed theoretically. Qualitative and quantitative experiments demonstrate that the proposed method is comparable to the supervised despeckling approaches on synthetic datasets and outperforms several state-of-the-art unsupervised methods on real SAR datasets.

Keywords

• Deep learning
• image despeckling
• Siamese network
• synthetic aperture radar (SAR)
• unsupervised training