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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050808 |
