A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation

Multi-pass synthetic aperture radar interferometry (InSAR) stack data denoising is a significant prerequisite for extracting geophysical parameters. InSAR stack data can be considered as a third-order tensor in the complex domain, and the process of tensor decomposition to acquire the low-rank tenso...

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Main Authors: Yanan You, Rui Wang, Wenli Zhou
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
KBR
Online Access:https://ieeexplore.ieee.org/document/8840850/
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spelling doaj-1ba7ae6834cf4de7ad3be8574feeb6a82021-04-05T17:30:35ZengIEEEIEEE Access2169-35362019-01-01713517613519110.1109/ACCESS.2019.29420088840850A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank RepresentationYanan You0https://orcid.org/0000-0001-6473-9187Rui Wang1https://orcid.org/0000-0002-6557-4078Wenli Zhou2Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, ChinaMulti-pass synthetic aperture radar interferometry (InSAR) stack data denoising is a significant prerequisite for extracting geophysical parameters. InSAR stack data can be considered as a third-order tensor in the complex domain, and the process of tensor decomposition to acquire the low-rank tensor has been employed as an effective interferometric phase filter for InSAR stack data. It is noted that the definition of tensor rank is the core of tensor-based filter. In this paper, we investigate the properties of Tucker rank, CANDECAMP/PARAFAC (CP) rank and Kronecker Basis Representation (KBR) in InSAR stack data, and then we found that it is suitable to extend KBR, as a hybrid tensor rank representation, into InSAR tensor filtering. Firstly, an improved InSAR phase tensor model is utilized to represent the phenomenon of interferometric phase, which perceives the observed InSAR phase tensor as the combination of low-rank, sparse noise and Gaussian noise tensors. Based on the principle of KBR, then the novel phase filtering method, named as KBR-InSAR, is proposed to decompose the complex InSAR tensor supported by the improved InSAR phase tensor model. With the comparison of other tensor filters, i.e. HoRPCA and WHoRPCA and the widespread traditional filters operating on a single interferometric pair, e.g. Goldstein, NL-SAR, NL-InSAR and InSAR-BM3D, it can be proved that the KBR-InSAR can efficiently reduce the noise with superior fringes preservation in the experiments on the simulated and real InSAR stack data collected from Sentinel-1B.https://ieeexplore.ieee.org/document/8840850/Synthetic aperture radar (SAR)SAR interferometry (InSAR)tensor decompositionKBRphase filtering
collection DOAJ
language English
format Article
sources DOAJ
author Yanan You
Rui Wang
Wenli Zhou
spellingShingle Yanan You
Rui Wang
Wenli Zhou
A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
IEEE Access
Synthetic aperture radar (SAR)
SAR interferometry (InSAR)
tensor decomposition
KBR
phase filtering
author_facet Yanan You
Rui Wang
Wenli Zhou
author_sort Yanan You
title A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
title_short A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
title_full A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
title_fullStr A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
title_full_unstemmed A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
title_sort phase filter for multi-pass insar stack data by hybrid tensor rank representation
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Multi-pass synthetic aperture radar interferometry (InSAR) stack data denoising is a significant prerequisite for extracting geophysical parameters. InSAR stack data can be considered as a third-order tensor in the complex domain, and the process of tensor decomposition to acquire the low-rank tensor has been employed as an effective interferometric phase filter for InSAR stack data. It is noted that the definition of tensor rank is the core of tensor-based filter. In this paper, we investigate the properties of Tucker rank, CANDECAMP/PARAFAC (CP) rank and Kronecker Basis Representation (KBR) in InSAR stack data, and then we found that it is suitable to extend KBR, as a hybrid tensor rank representation, into InSAR tensor filtering. Firstly, an improved InSAR phase tensor model is utilized to represent the phenomenon of interferometric phase, which perceives the observed InSAR phase tensor as the combination of low-rank, sparse noise and Gaussian noise tensors. Based on the principle of KBR, then the novel phase filtering method, named as KBR-InSAR, is proposed to decompose the complex InSAR tensor supported by the improved InSAR phase tensor model. With the comparison of other tensor filters, i.e. HoRPCA and WHoRPCA and the widespread traditional filters operating on a single interferometric pair, e.g. Goldstein, NL-SAR, NL-InSAR and InSAR-BM3D, it can be proved that the KBR-InSAR can efficiently reduce the noise with superior fringes preservation in the experiments on the simulated and real InSAR stack data collected from Sentinel-1B.
topic Synthetic aperture radar (SAR)
SAR interferometry (InSAR)
tensor decomposition
KBR
phase filtering
url https://ieeexplore.ieee.org/document/8840850/
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