A compensation method for a time-space variant atmospheric phase applied to time-series GB-SAR images

Cheng Hu, Yunkai Deng, Weiming Tian*, Zheng Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

An atmospheric effect is a main error source that affects interferometric measurements. When a ground-based multiple-input multiple-output (GB-MIMO) radar, i.e., a specific type of GB-synthetic aperture radar (GB-SAR), was utilized to continuously monitor an open-pit mine, the interferometric phases of some interferograms were complexly space-variant due to time-variant weather conditions. The conventional method of atmospheric phase (AP) compensation was no longer applicable. This paper proposes an improved compensation method of a time-space variant AP applied to time-series GB-SAR images. The permanent scatterers (PSs) were classified into three types based on their different spatial properties: The noise-dominant PS (NPS), the deformation-dominant PS (DPS), and the atmospheric effect-dominant PS (APS). The NPSs were firstly rejected based on the differential phase analysis of neighboring PSs. The DPSs were then rejected based on the cluster partition and selection. With the APSs, the space-variant AP was estimated with a spatial interpolation. To validate the feasibility of the proposed method, short-term and long-term experimental datasets were processed. Comparisons with a conventional method proved that the proposed method can well reduce AP errors and avoid the misunderstanding of motional areas.

Original languageEnglish
Article number2350
JournalRemote Sensing
Volume11
Issue number20
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Atmospheric effect-dominant PS
  • Atmospheric phase
  • Deformation-dominant PS
  • GB-SAR
  • Noise-dominant PS
  • Permanent scatterer (PS)
  • Time-space variation

Fingerprint

Dive into the research topics of 'A compensation method for a time-space variant atmospheric phase applied to time-series GB-SAR images'. Together they form a unique fingerprint.

Cite this