MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR
Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2024-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR23185 |
| _version_ | 1797354391733272576 |
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| author | Zegang DING Tao SUN Zhen WANG Jian ZHAO Yipeng SHI Haolong CHEN Zhizhou CHEN Yan WANG Tao ZENG |
| author_facet | Zegang DING Tao SUN Zhen WANG Jian ZHAO Yipeng SHI Haolong CHEN Zhizhou CHEN Yan WANG Tao ZENG |
| author_sort | Zegang DING |
| collection | DOAJ |
| description | Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data. |
| first_indexed | 2024-03-08T13:48:49Z |
| format | Article |
| id | doaj.art-eb91b097c78c430aa1806521e60ee47e |
| institution | Directory Open Access Journal |
| issn | 2095-283X |
| language | English |
| last_indexed | 2024-03-08T13:48:49Z |
| publishDate | 2024-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-eb91b097c78c430aa1806521e60ee47e2024-01-16T07:27:39ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2024-02-011319711510.12000/JR23185R23185MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SARZegang DING0Tao SUN1Zhen WANG2Jian ZHAO3Yipeng SHI4Haolong CHEN5Zhizhou CHEN6Yan WANG7Tao ZENG8Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaInterferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data.https://radars.ac.cn/cn/article/doi/10.12000/JR23185interferometric synthetic aperture radar (insar)multi-channelphase unwrapping (pu)deep learningunet |
| spellingShingle | Zegang DING Tao SUN Zhen WANG Jian ZHAO Yipeng SHI Haolong CHEN Zhizhou CHEN Yan WANG Tao ZENG MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR Leida xuebao interferometric synthetic aperture radar (insar) multi-channel phase unwrapping (pu) deep learning unet |
| title | MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR |
| title_full | MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR |
| title_fullStr | MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR |
| title_full_unstemmed | MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR |
| title_short | MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR |
| title_sort | mcj unet a dual multi channel joint phase unwrapping network for interferometric sar |
| topic | interferometric synthetic aperture radar (insar) multi-channel phase unwrapping (pu) deep learning unet |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR23185 |
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