A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Yuan, Ye, Guan, Jian, Feng, Pengming, Wu, Yanxia
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial learning; Generators; image despeckling; Iterative methods; Learning; Nested-UNet; Network architecture; Noise2Noise (N2N); Radar polarimetry; SAR (radar); Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3034470

In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed. First, an adversarial learning framework is designed to generate speckled-to-speckled (S2S) image pairs from the same scene in the situation where only single speckled SAR images are available. Then, the S2S SAR image pairs are employed to train a modified despeckling Nested-UNet model using the Noise2Noise (N2N) strategy. Moreover, an iterative version of the PSD method (PSDi) is also presented. Experiments are conducted on both synthetic speckled and real SAR data to demonstrate the superiority of the proposed methods compared with several state-of-the-art methods. The results show that our methods can reach a good tradeoff between feature preservation and speckle suppression.