A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not alw...

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Autores principales:	Liming Pu, Xiaoling Zhang, Zenan Zhou, Liang Li, Liming Zhou, Jun Shi, Shunjun Wei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	interferometric synthetic aperture radar deep convolutional neural network phase unwrapping Science Q
Acceso en línea:	https://doaj.org/article/498b800f92714ed689fc8b3bb2c088d7
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id	oai:doaj.org-article:498b800f92714ed689fc8b3bb2c088d7
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spelling	oai:doaj.org-article:498b800f92714ed689fc8b3bb2c088d72021-11-25T18:54:25ZA Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network10.3390/rs132245642072-4292https://doaj.org/article/498b800f92714ed689fc8b3bb2c088d72021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4564https://doaj.org/toc/2072-4292Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.Liming PuXiaoling ZhangZenan ZhouLiang LiLiming ZhouJun ShiShunjun WeiMDPI AGarticleinterferometric synthetic aperture radardeep convolutional neural networkphase unwrappingScienceQENRemote Sensing, Vol 13, Iss 4564, p 4564 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	interferometric synthetic aperture radar deep convolutional neural network phase unwrapping Science Q
spellingShingle	interferometric synthetic aperture radar deep convolutional neural network phase unwrapping Science Q Liming Pu Xiaoling Zhang Zenan Zhou Liang Li Liming Zhou Jun Shi Shunjun Wei A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
description	Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.
format	article
author	Liming Pu Xiaoling Zhang Zenan Zhou Liang Li Liming Zhou Jun Shi Shunjun Wei
author_facet	Liming Pu Xiaoling Zhang Zenan Zhou Liang Li Liming Zhou Jun Shi Shunjun Wei
author_sort	Liming Pu
title	A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
title_short	A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
title_full	A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
title_fullStr	A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
title_full_unstemmed	A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
title_sort	robust insar phase unwrapping method via phase gradient estimation network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/498b800f92714ed689fc8b3bb2c088d7
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A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

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