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Open AccessArticle

SAR Interferometric Baseline Refinement Based on Flat-Earth Phase without a Ground Control Point

School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
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Remote Sens. 2020, 12(2), 233; https://doi.org/10.3390/rs12020233
Received: 29 November 2019 / Revised: 4 January 2020 / Accepted: 8 January 2020 / Published: 9 January 2020
Interferometric baseline estimation is a key procedure of interferometric synthetic aperture radar (SAR) data processing. The error of the interferometric baseline affects not only the removal of the flat-earth phase, but also the transformation coefficient between the topographic phase and elevation, which will affect the topographic phase removal for differential interferometric SAR (D-InSAR) and the accuracy of the final generated digital elevation model (DEM) product for interferometric synthetic aperture (InSAR). To obtain a highly accurate interferometric baseline, this paper firstly investigates the geometry of InSAR imaging and establishes a rigorous relationship between the interferometric baseline and the flat-earth phase. Then, a baseline refinement method without a ground control point (GCP) is proposed, where a relevant theoretical model and resolving method are developed. Synthetic and real SAR datasets are used in the experiments, and a comparison with the conventional least-square (LS) baseline refinement method is made. The results demonstrate that the proposed method exhibits an obvious improvement over the conventional LS method, with percentages of up to 51.5% in the cross-track direction. Therefore, the proposed method is effective and advantageous. View Full-Text
Keywords: synthetic aperture radar interferometry; InSAR baseline estimation; flat-earth phase; baseline refinement synthetic aperture radar interferometry; InSAR baseline estimation; flat-earth phase; baseline refinement
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Xu, B.; Li, Z.; Zhu, Y.; Shi, J.; Feng, G. SAR Interferometric Baseline Refinement Based on Flat-Earth Phase without a Ground Control Point. Remote Sens. 2020, 12, 233.

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