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

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...

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Autores principales:	Gang Zhang, Zhi Li, Xuewei Li, Sitong Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net Science Q
Acceso en línea:	https://doaj.org/article/af7902b8d2054e7d8b7a51424ef8c487
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id	oai:doaj.org-article:af7902b8d2054e7d8b7a51424ef8c487
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spelling	oai:doaj.org-article:af7902b8d2054e7d8b7a51424ef8c4872021-11-11T18:55:11ZSelf-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images10.3390/rs132143832072-4292https://doaj.org/article/af7902b8d2054e7d8b7a51424ef8c4872021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4383https://doaj.org/toc/2072-4292Self-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.Gang ZhangZhi LiXuewei LiSitong LiuMDPI AGarticleself-supervisedsynthetic aperture radar (SAR)despecklingenhanced U-NetScienceQENRemote Sensing, Vol 13, Iss 4383, p 4383 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net Science Q
spellingShingle	self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net Science Q Gang Zhang Zhi Li Xuewei Li Sitong Liu Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
description	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.
format	article
author	Gang Zhang Zhi Li Xuewei Li Sitong Liu
author_facet	Gang Zhang Zhi Li Xuewei Li Sitong Liu
author_sort	Gang Zhang
title	Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
title_short	Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
title_full	Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
title_fullStr	Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
title_full_unstemmed	Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
title_sort	self-supervised despeckling algorithm with an enhanced u-net for synthetic aperture radar images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/af7902b8d2054e7d8b7a51424ef8c487
work_keys_str_mv	AT gangzhang selfsuperviseddespecklingalgorithmwithanenhancedunetforsyntheticapertureradarimages AT zhili selfsuperviseddespecklingalgorithmwithanenhancedunetforsyntheticapertureradarimages AT xueweili selfsuperviseddespecklingalgorithmwithanenhancedunetforsyntheticapertureradarimages AT sitongliu selfsuperviseddespecklingalgorithmwithanenhancedunetforsyntheticapertureradarimages
_version_	1718431666417958912

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

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