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Franz J. Meyer

Researcher at University of Alaska Fairbanks

Publications -  139
Citations -  2584

Franz J. Meyer is an academic researcher from University of Alaska Fairbanks. The author has contributed to research in topics: Synthetic aperture radar & Interferometric synthetic aperture radar. The author has an hindex of 24, co-authored 137 publications receiving 2004 citations. Previous affiliations of Franz J. Meyer include German Aerospace Center.

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The Potential of Low-Frequency SAR Systems for Mapping Ionospheric TEC Distributions

TL;DR: The potential of broadband L-band SAR systems for ionospheric TEC mapping is studied and it is shown that phase advance and group delay can be measured by interferometric and correlation techniques, respectively.
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Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

TL;DR: The study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions.
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Prediction, Detection, and Correction of Faraday Rotation in Full-Polarimetric L-Band SAR Data

TL;DR: Methods for estimating Faraday rotation effects from PALSAR data are presented and the first unambiguous detection of FR in SAR data is presented, allowing the measurement of FR with high precision in areas where such measurements were previously inaccessible.
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Processing of Bistatic SAR Data From Quasi-Stationary Configurations

TL;DR: Two solutions for bistatic SAR data processing under the restriction of quasi-stationarity are offered, i.e., sufficiently equal velocity vectors of transmitter and receiver and a general approach named "NuSAR" is proposed, where the involved transfer functions are replaced by numerically computed ones.
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Correction and Characterization of Radio Frequency Interference Signatures in L-Band Synthetic Aperture Radar Data

TL;DR: A processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations, and the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of R FI-related information that allows for mapping, characterization, and classifying RFI signature across large spatial scales.