Synthetic Aperture Radar Phase Unwrapping Research Articles

Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed approach is currently the only phase unwrapping approach to take this strategy with its quality assurance. The proposed approach was found to improve upon the solution quality of the RGPU approach, in some cases achieving a tenfold decrease in root-mean-square error for simulated data. The PBRGPU approach performed well when applied to RADARSAT-2 data collected over Polar Bear Provincial Park (Ontario, Canada). The PBRGPU solutions were consistently on par with or superior to those generated by SNAPHU in terms of accuracy. While the PBRGPU approach does lag behind SNAPHU in terms of the domain of the solution, with SNAPHU unwrapping a significantly larger portion of the interferogram in all test cases, this issue could readily be mitigated through post-processing of the unwrapped interferogram. The proposed approach provides a solid foundation for region-growing algorithms that adapt to local noise levels and integrate all available information rather than relying on preprocessing strategies.

A Multibaseline InSAR Phase Unwrapping Method Using Designed Optimal Baselines Obtained by Motion Compensation Algorithm

Multibaseline (MB) interferometric synthetic aperture radar phase unwrapping (PU) has the advantage of using the baseline diversity to increase the ambiguity intervals of the interferometric phases and overcome the limitation of the phase continuity assumption. One major challenge of the MB PU is that the performance of PU greatly depends on the baseline configuration. In this letter, the motion compensation algorithm is used to modify the baselines of the interferograms. Therefore, optimal baseline configuration can be used in the MB PU to improve the PU performance. Real ALOS/PALSAR data over Himalayan mountain area have been used to verify the performance of the proposed method. The experimental results show that the MB PU with modified baselines becomes more reliable.

Comparison of optimization algorithms for interferometric synthetic aperture radar phase unwrapping based on identical Markov random fields

Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth’s surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms—LBP and TRW-S—perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm).

Interferometric synthetic aperture radar phase unwrapping based on sparse Markov random fields by graph cuts

Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. It is known that two-dimensional (2-D) PU problems can be formulated as maximum a posteriori estimation of Markov random fields (MRFs). However, considering that the traditional MRF algorithm is usually defined on a rectangular grid, it fails easily if large parts of the wrapped data are dominated by noise caused by large low-coherence area or rapid-topography variation. A PU solution based on sparse MRF is presented to extend the traditional MRF algorithm to deal with sparse data, which allows the unwrapping of InSAR data dominated by high phase noise. To speed up the graph cuts algorithm for sparse MRF, we designed dual elementary graphs and merged them to obtain the Delaunay triangle graph, which is used to minimize the energy function efficiently. The experiments on simulated and real data, compared with other existing algorithms, both confirm the effectiveness of the proposed MRF approach, which suffers less from decorrelation effects caused by large low-coherence area or rapid-topography variation.

Interferometric SAR phase unwrapping using the finite element method

The authors present a new two-dimensional algorithm for interferometric synthetic aperture radar phase unwrapping based on the finite element method. The proposed technique allows an efficient least-squares solution carried out in time domain. Weighting functions can be introduced without increasing computational requirements. The possibility to speed up computations via a multigrid technique is discussed. A noise-robust extension of the proposed technique is also investigated. Presented experiments on real ERS-1 data, as well as on simulated patterns, demonstrate the precision and efficiency of the procedure.