FPUNet: A Fourier-Enhanced U-Net for Robust 2-D Phase Unwrapping of Noisy InSAR Interferograms.
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| Title: | FPUNet: A Fourier-Enhanced U-Net for Robust 2-D Phase Unwrapping of Noisy InSAR Interferograms. |
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| Authors: | He, Yuxiao1,2 (AUTHOR), Wu, Yuming1,2 (AUTHOR) wuym@lreis.ac.cn, Gao, Xing1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p808. 29p. |
| Subjects: | Phase unwrapping (Digital image processing), Radar interferometry, Frequency-domain analysis, Deep learning, Signal denoising |
| Abstract: | Highlights: What are the main findings? We propose FPUNet, a Fourier-enhanced phase unwrapping network that integrates frequency-domain global context modeling with multi-scale spatial feature aggregation for robust 2-D unwrapping under steep deformation gradients and multi-source noise. FPUNet improves unwrapping fidelity while preserving physical consistency, as validated by rewrapping checks and closed-loop phase consistency on synthetic and real interferograms. What are the implications of the main findings? The proposed framework provides a practical learning-based alternative to classical unwrapping pipelines for challenging coseismic/complex scenes, with fast inference suitable for large-scale InSAR processing. The physics-informed constraints offer a general strategy to regularize deep unwrapping models, enabling reliable deformation retrieval when the Itoh condition is frequently violated. Two-dimensional phase unwrapping (PU) of interferometric synthetic aperture radar (InSAR) data remains difficult when steep deformation gradients and multi-source disturbances violate the Itoh condition. This study proposes FPUNet, a Fourier-enhanced encoder–decoder for joint denoising and 2-D PU, in which frequency-domain global context modeling is combined with complementary multi-scale spatial aggregation and attention-based feature refinement. Specifically, the bottleneck cascades a Fourier Mixed Residual Block (FMRB), atrous spatial pyramid pooling (ASPP), and a convolutional block attention module (CBAM) to suppress noise while preserving deformation-related fringe structures. FPUNet is trained end-to-end on realistically simulated Sentinel-1 interferograms generated from Shuttle Radar Topography Mission (SRTM) digital elevation models using a physics-informed composite loss that enforces data fidelity, gradient consistency, spectral regularization, and selective rewrapping consistency. On a synthetic benchmark of 1800 test interferograms, FPUNet achieves an RMSE of 0.79 rad, improving over a plain U-Net (1.61 rad) and producing fewer large fringe-number errors than least-squares, SNAPHU, PUNet, and DLPU. Experiments on real Sentinel-1 data over the Datong mining area and the 2022 Menyuan and Luding earthquakes further indicate improved phase closure and rewrapping consistency, particularly in high-gradient coseismic fringes, supporting FPUNet as a robust PU module for InSAR deformation monitoring. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192640065 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: FPUNet: A Fourier-Enhanced U-Net for Robust 2-D Phase Unwrapping of Noisy InSAR Interferograms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22He%2C+Yuxiao%22">He, Yuxiao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yuming%22">Wu, Yuming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wuym@lreis.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Xing%22">Gao, Xing</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p808. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Phase+unwrapping+%28Digital+image+processing%29%22">Phase unwrapping (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Frequency-domain+analysis%22">Frequency-domain analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? We propose FPUNet, a Fourier-enhanced phase unwrapping network that integrates frequency-domain global context modeling with multi-scale spatial feature aggregation for robust 2-D unwrapping under steep deformation gradients and multi-source noise. FPUNet improves unwrapping fidelity while preserving physical consistency, as validated by rewrapping checks and closed-loop phase consistency on synthetic and real interferograms. What are the implications of the main findings? The proposed framework provides a practical learning-based alternative to classical unwrapping pipelines for challenging coseismic/complex scenes, with fast inference suitable for large-scale InSAR processing. The physics-informed constraints offer a general strategy to regularize deep unwrapping models, enabling reliable deformation retrieval when the Itoh condition is frequently violated. Two-dimensional phase unwrapping (PU) of interferometric synthetic aperture radar (InSAR) data remains difficult when steep deformation gradients and multi-source disturbances violate the Itoh condition. This study proposes FPUNet, a Fourier-enhanced encoder–decoder for joint denoising and 2-D PU, in which frequency-domain global context modeling is combined with complementary multi-scale spatial aggregation and attention-based feature refinement. Specifically, the bottleneck cascades a Fourier Mixed Residual Block (FMRB), atrous spatial pyramid pooling (ASPP), and a convolutional block attention module (CBAM) to suppress noise while preserving deformation-related fringe structures. FPUNet is trained end-to-end on realistically simulated Sentinel-1 interferograms generated from Shuttle Radar Topography Mission (SRTM) digital elevation models using a physics-informed composite loss that enforces data fidelity, gradient consistency, spectral regularization, and selective rewrapping consistency. On a synthetic benchmark of 1800 test interferograms, FPUNet achieves an RMSE of 0.79 rad, improving over a plain U-Net (1.61 rad) and producing fewer large fringe-number errors than least-squares, SNAPHU, PUNet, and DLPU. Experiments on real Sentinel-1 data over the Datong mining area and the 2022 Menyuan and Luding earthquakes further indicate improved phase closure and rewrapping consistency, particularly in high-gradient coseismic fringes, supporting FPUNet as a robust PU module for InSAR deformation monitoring. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18050808 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 808 Subjects: – SubjectFull: Phase unwrapping (Digital image processing) Type: general – SubjectFull: Radar interferometry Type: general – SubjectFull: Frequency-domain analysis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Signal denoising Type: general Titles: – TitleFull: FPUNet: A Fourier-Enhanced U-Net for Robust 2-D Phase Unwrapping of Noisy InSAR Interferograms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: He, Yuxiao – PersonEntity: Name: NameFull: Wu, Yuming – PersonEntity: Name: NameFull: Gao, Xing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
