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

TL;DR

This paper introduces a practical SAR despeckling approach that uses adversarial learning to generate training pairs from single speckled images, enabling effective despeckling without clean references.

Contribution

The paper proposes a novel adversarial learning framework to generate speckled-to-speckled image pairs and trains a modified Nested-UNet model with Noise2Noise strategy for SAR despeckling.

Findings

Outperforms several state-of-the-art despeckling methods.

Effectively balances feature preservation and speckle suppression.

Works on both synthetic and real SAR data.

Abstract

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.