Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization.

By Curtis W. Chen, Howard Allan Zebker. Journal of The Optical Society of America A-optics Image Science and Vision, 2001

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Fast, automated, N‐dimensional phase‐unwrapping algorithm (2003, methodology): “The generalization of some other methods can be difficult because they use application-specific knowledge, such as the coherence function in SAR imaging (8,9), multiple MR images for chemical shift mapping (10), or prior expectations about velocities (11,12).”
Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction (2019, methodology): “The first step is to identify reliable regions using the connected component information from the phase unwrapping algorithm such as SNAPHU (Chen and Zebker, 2001).”
Phase Unwrapping in InSAR : A Review (2019, methodology): “It is worth mentioning that the SNAPHU method proposed in [63] is one of the most representative statistics-based methods; it uses a statistical-cost, network-flow model to compute the most likely PU solution.”
Phase unwrapping for large SAR interferograms: statistical segmentation and generalized network models (2002, methodology): “we propose a technique for applying Chen and Zebker’s [5] statistical-cost, network-flow phase-unwrapping algorithm (SNAPHU) to large datasets.”
Phase unwrapping in three dimensions with application to InSAR time series. (2007, neutral): “, minimum-cost flow [3], statistical-cost network-flow [5], and iterative least squares, which is described in this section.”