Abstract
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.
Highlights
Interferometric synthetic aperture radar (InSAR) is a powerful tool to measure the digital elevation model (DEM) of the earth’s surface.[1]
In order to analyze the accuracy of the proposed Markov random fields (MRFs) algorithm, simulated data are used in the experiment, which are obtained from an interferogram simulated by the MATLAB toolbox for InSAR.[24]
We proposed a phase unwrapping (PU) solution based on sparse MRF for extending the traditional regular-grid MRF PU algorithm dealing with a sparse data to process InSAR interferograms for the generation of DEM
Summary
Interferometric synthetic aperture radar (InSAR) is a powerful tool to measure the digital elevation model (DEM) of the earth’s surface.[1]. Path following algorithms apply line integration schemes over the wrapped phase image, and basically rely on the assumption that the Itoh condition holds along the integration path This approach includes the so-called branch cuts,[3,4] quality-guided,[5] and minimum discontinuity,[6] etc. The Bayesian approach relies on a data observation mechanism model, as well as a priori knowledge of the phase to be modeled.[9,10,11] Based on this approach, Markov random fields (MRFs) constraining global phase variations are used for solving a 2-D PU problem, which has been proposed in several studies.[12,13,14] Ying et al.[12] proposed an MRF approach for a 2-D PU problem This approach utilizes an efficient algorithm for parameter estimation using a series of dynamic programming connected by the iterated conditional modes.
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