A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers
Abstract
:1. Introduction
2. Method
2.1. TS(Pol)InSAR Data Generation
2.2. TS(Pol)InSAR Coherency Matrix Statistic and Equivalent Number of Looks (ENL) Estimation
2.3. Phase Linking (PL) Theory
2.4. Cramer–Rao Lower Bound (CRLB) for Estimating ESM Interferometric Phases and Research Motivation
2.5. Proposed TSTP Coherency Matrix Construction and Theoretical Interpretation
- The TSIn scattering vector of each Pauli basis polarimetric channel is generated from the time-series full polarimetric SLC stack according to Equation (4), whose reference phase components are removed by the external DEM and the orbital information.
- The TSIn coherency matrix of each Pauli basis polarimetric channel is generated from the corresponding TSIn scattering vector according to Equation (4), and a spatial sample average estimation is performed on each TSIn coherency matrix.
- The TSTP coherency matrix is constructed by the polarization stacking operation in Equation (17) and normalized to obtain the corresponding TSCoh matrix.
- As an efficient MLE of PL, the EMI method [20] is selected to obtain multiple eigenvectors from the normalized TSTP coherency matrix according to Equation (13).
- Finally, the ESM interferometric phases can be extracted from the phases of the optimal eigenvector corresponding to the minimum eigenvector under the premise that a certain image is the master one.
3. Result
3.1. Simulated Experimental Data Simulation and Parameter Setting
3.2. Simulated Experimental Result
3.3. Real Experimental Data Description and Parameter Setting
3.4. Real Experimental Result
4. Discussion
4.1. Simulated Experimental Discussion under Different Multilooking Size and Decorrelation Degrees
4.2. Real Experimental Discussion of The Full Scene
4.3. Real Experimental Discussion of The Selected ROI
4.4. Computational Efficiency Comparison between Two Polarimetric Optimization Methods
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor | Polarization | Number of Images | Frequency | Range Spacing | Azimuth Spacing | Incident Angle |
---|---|---|---|---|---|---|
ALOS PALSAR-2 | Full | 12 | 1.24 GHz | 2.86 m | 3.05 m | 33.24° |
Acquisition Time | Perpendicular Baseline (m) | Temporal Baseline (Day) |
---|---|---|
20150324 | 29.5 | −756 |
20160209 | 30.2 | −434 |
20160223 | −260.4 | −420 |
20160809 | −33.0 | −252 |
20161004 | 26.6 | −196 |
20161101 | 28.1 | −168 |
20170124 | −51.8 | −84 |
20170404 | 225.7 | −14 |
20170418 | 0 | 0 |
20170822 | −64.7 | 126 |
20180821 | 6.0 | 490 |
20190625 | −112.4 | 798 |
Method | ALOS PALSAR-2 (1200 × 1000 pixels) |
---|---|
ESPO | 1,408,181.04 s |
TSTP | 2.28 s |
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Shen, P.; Wang, C.; Hu, J. A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers. Remote Sens. 2022, 14, 4168. https://doi.org/10.3390/rs14174168
Shen P, Wang C, Hu J. A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers. Remote Sensing. 2022; 14(17):4168. https://doi.org/10.3390/rs14174168
Chicago/Turabian StyleShen, Peng, Changcheng Wang, and Jun Hu. 2022. "A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers" Remote Sensing 14, no. 17: 4168. https://doi.org/10.3390/rs14174168