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Time Series Phase Unwrapping Based on Graph Theory and Compressed Sensing
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  • Zhangfeng Ma,
  • Mi Jiang,
  • Mostafa Khoshmanesh,
  • Xiao Cheng
Zhangfeng Ma
Hohai University

Corresponding Author:jspcmazhangfeng@hhu.edu.cn

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Mi Jiang
Sun Yat-Sen University
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Mostafa Khoshmanesh
California Institute of Technology
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Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-Sen University
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We present a new time series PU approach to improve the unwrapping accuracy in this article. The rationale behind is to first improve the sparse unwrapping by mitigating the phase gradient in a 2D network and then correcting the unwrapping errors in time based on the triplet phase closure. Rather than commonly-used Delaunay network, we employ All-Pairs-Shortest-Path (APSP) algorithm in graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2D spatial domain. Next, we formulate the PU error correction in the 1D temporal domain as a compressed sensing (CS) problem, according to the sparsity rule of remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of Integer Linear Programming. The comprehensive comparisons on synthetic and real Sentinel-1 data covering Lost Hills, California confirm the validity of the proposed 2D+1D unwrapping approach.
2022Published in IEEE Transactions on Geoscience and Remote Sensing volume 60 on pages 1-12. 10.1109/TGRS.2021.3066784