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| Citation: | DING Zegang, SUN Tao, WANG Zhen, et al. MCJ-UNet: A dual/multi-channel-joint phase unwrapping network for interferometric SAR[J]. Journal of Radars, 2024, 13(1): 97–115. doi: 10.12000/JR23185
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MCJ-UNet: A Dual/Multi-channel-joint Phase Unwrapping Network for Interferometric SAR
doi: 10.12000/JR23185
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Abstract
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. -
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References
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- Figure 1. Schematic diagram of phase unwrapping
- Figure 2. Schematic diagram of the ambiguity number clustering
- Figure 3. The structure of MCJ-UNet network
- Figure 4. SE module
- Figure 5. Flowchart of network training
- Figure 6. Schematic diagram of simulated terrain construction
- Figure 7. Comparison of wrapped phase before and after adding texture
- Figure 8. Multi-channel wrapped phase and label
- Figure 9. Simulated DEM and SNR distribution
- Figure 10. Reference phase of dual frequency channels
- Figure 11. The interferograms of dual-frequency channels
- Figure 12. Ambiguity number estimation results obtained based on MCJ-UNet
- Figure 13. Comparison of results for different methods on simulated data
- Figure 14. Reference terrain height and multi-frequency interferograms
- Figure 15. Reference phase and comparison of unwrapped phase obtained by different methods
- Figure 16. Real InSAR data of multi-baseline
- Figure 17. Comparison of unwrapped phase obtained by different methods for real data