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Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization
1Department of Electrical Engineering, Stanford University, California 94305-9515, USA. curtis@nova.stanford.edu
Summary
This study introduces a novel maximum a posteriori probability (MAP) estimation approach for two-dimensional (2-D) phase unwrapping in interferometric radar. The developed algorithm provides more complete and accurate phase-unwrapped solutions than existing methods.
Area of Science:
- Geospatial analysis
- Remote sensing technology
- Signal processing
Background:
- Interferometric radar requires two-dimensional (2-D) phase unwrapping to estimate unambiguous phase data.
- Current methods face challenges with accuracy and completeness, especially in complex terrains or deformation mapping.
Purpose of the Study:
- To develop a robust Maximum A Posteriori probability (MAP) estimation approach for 2-D phase unwrapping.
- To create an algorithm that improves the accuracy and completeness of phase-unwrapped solutions in interferometric radar data.
Main Methods:
- Developed a MAP estimation framework for 2-D phase unwrapping.
- Derived models for joint statistics of estimated and observed signals in topographic and differential interferometry.
- Employed nonlinear network-flow techniques to approximate MAP solutions using generalized cost functions.
Main Results:
- The MAP-based algorithm achieved complete and more accurate phase-unwrapped solutions compared to other tested algorithms.
- Successfully applied to challenging topographic interferograms with rough terrain and layover.
- Demonstrated effectiveness on differential interferograms for measuring earthquake-induced deformation.
Conclusions:
- The proposed MAP estimation approach offers a significant advancement in 2-D phase unwrapping for interferometric radar.
- The algorithm provides reliable and accurate results for both topographic mapping and deformation monitoring applications.