Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

Remote Sensing

Gang Zhang¹  Zhi Li¹  Sitong Liu¹  Xuewei Li² 

[1] Department of Aerospace Science and Technology, Space Engineering University, Bayi Road, Huairou District, Beijing 101416, China;Institute of Software, Chinese Academy of Sciences, No. 4, South Fourth Street, Zhongguancun, Haidian District, Beijing 100190, China;

关键词: self-supervised;    synthetic aperture radar (SAR);    despeckling;    enhanced U-Net;   

DOI  :  10.3390/rs13214383

来源: DOAJ

【 摘 要 】

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

【 授权许可】

Unknown