New Interferometric Phase Unwrapping Method Based on Energy Minimization From Contextual Modeling

Tlili, Ayoub; Cavayas, François; Foucher, Samuel; Siles, Gabriela Lianet

doi:10.1109/jstars.2020.3043344

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Lately, Yu et al [20] proposed a novel minimum-infinity-norm-based PU method that could not only keep the fringe-congruency in the high-quality regions, but also filter the input phase fringes in the noisy regions. Tlili et al [21] proposed a PU method based on energy minimization from contextual modeling, which considered both local and global constraints of the interferogram. Gao et al [22] proposed a phase-slicing 2-D PU method using the L 1 -norm, which could effectively reduce the phase gradient interval.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Square-Root Unscented Kalman Filter Phase Unwrapping with Modified Phase Gradient Estimation

Zhang

Gao

et al. 2022

Remote Sensing

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Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness.

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Section: Introductionmentioning

confidence: 99%