An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR

Zihao Lin, Yan Duan, Yunkai Deng, Weiming Tian*, Zheng Zhao

*Corresponding author for this work

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Abstract

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method.

Original languageEnglish
Article number2543
JournalRemote Sensing
Volume14
Issue number11
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • ground-based interferometric synthetic aperture radar (GB-InSAR)
  • minimum cost flow (MCF)
  • multi-baseline phase unwrapping (MB PU)
  • spatial-distribution-based denoising algorithm (SD-based DA)
  • triangulation network

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Lin, Z., Duan, Y., Deng, Y., Tian, W., & Zhao, Z. (2022). An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR. Remote Sensing, 14(11), Article 2543. https://doi.org/10.3390/rs14112543