Abstract
Multi-baseline (MB) phase unwrapping (PU) is a key step of MB synthetic aperture radar (SAR) interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU is applicable to the area where topography varies violently without obeying the phase continuity assumption. A two-stage programming MB PU approach (TSPA) proposed by H. Yu. builds the link between SB and MB PUs, so many existing classical SB PU methods can be transplanted into the MB domain. In this paper, an extended PU max-flow/min-cut (PUMA) algorithm for MB InSAR using the TSPA, referred to as TSPA-PUMA, is proposed, consisting of a two-stage programming procedure. In stage 1, phase gradients are estimated based on Chinese remainder theorem (CRT). In stage 2, a Markov random field (MRF) model of PUMA is designed for modeling local contextual dependence based on the phase gradients obtained by stage 1. Subsequently, the energy of the MRF model is minimized by graph cuts techniques. The experiment results illustrate that the TSPA-PUMA method can drastically enhance the accuracy of the original PUMA method in the rugged area, and is more efficient than the original TSPA method. In addition, the noise robustness of TSPA-PUMA can be improved through adding more interferograms with different baseline lengths.
Highlights
Interferometric synthetic aperture radar (InSAR) is a powerful tool to reconstruct the digital elevation model (DEM) or surface deformation of the Earth’s surface [1]
The results show that the two-stage programming MB PU approach (TSPA)-phase unwrapping max-flow/min-cut (PUMA) method can significantly improve the phase unwrapping (PU) accuracy of the original PUMA algorithm in the rugged and mountainous area, and the noise robustness of TSPA-PUMA can be improved if employing more interferograms with different baseline lengths
Based on the gradient information obtained by Equation (9), the energy minimization framework based on the Markov random field (MRF) model for TSPA-PUMA respectively obtain the final PU solution of each interferogram r, which is obtained by Equation (10), arg min kr ( s )
Summary
Interferometric synthetic aperture radar (InSAR) is a powerful tool to reconstruct the digital elevation model (DEM) or surface deformation of the Earth’s surface [1]. This algorithm uses a new energy minimization framework, which is based on the Markov random field (MRF) Under this condition, the problem of ambiguity number estimation can be translated into computing a sequence of binary optimizations (i.e., {0, 1}-cut), which can be solved by graph cuts techniques. The problem of ambiguity number estimation can be translated into computing a sequence of binary optimizations (i.e., {0, 1}-cut), which can be solved by graph cuts techniques The reason why this algorithm is so popular is that the MRF model allows a large family of potential functions (i.e., consisting of convex potential and non-convex potential), which gives flexibility to handle effectively both continuous and discontinuous phase features.
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