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Record W2003244310 · doi:10.1109/tgrs.2012.2201946

High-Precision Assessment and Calibration of Polarimetric RADARSAT-2 SAR Using Transponder Measurements

2012· article· en· W2003244310 on OpenAlexafffund
R. Touzi, R.K. Hawkins, Stéphane Côté

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsCanadian Space AgencyNatural Resources Canada
FundersCanadian Space Agency
KeywordsCalibrationTransponder (aeronautics)Remote sensingSynthetic aperture radarComputer scienceCrosstalkPolarimetryPhysicsOpticsGeologyScattering

Abstract

fetched live from OpenAlex

Independent assessment and calibration of polarimetric RADARSAT-2 (RS2) synthetic aperture radar (SAR) are conducted using “uncalibrated” data collected at various incidence angles (from 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> to 40 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ). Analysis of the response of a transponder deployed at HV and VH configurations permits high-precision measurement of RS2 antenna crosstalks. It is shown that the RS2 antenna is highly isolated (better than -32 dB) with crosstalks stable with incidence angle. A new calibration method based on transponder measurements is introduced. It is shown that the transponder calibration method removes almost completely the low antenna crosstalk with a residual crosstalk lower than -43 dB. Only one transmitter-receiver distortion matrix measured at a given incidence angle is required for accurate calibration of the 20 polarimetric modes of the RS2 SAR from 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> to 40 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> . Uncalibrated RS2 single-look complex (SLC) data with real and imaginary parts provided in 32-bit floating point are required for an effective application of the transponder calibration method. RS2 “calibrated” data are also considered for the assessment of the actual RS2 polarimetric calibration. It is shown that RS2 calibration meets comfortably the CEOS Cal-Val requirements with a residual crosstalk lower than -32 dB. The RS2 calibration accuracy does depend on the mode (i.e., incidence angle) with residual crosstalk that varies between - 32 and -43 dB. To assess the impact of the residual crosstalk on polarimetric applications, the RS2 data are recalibrated using transponder measurements. Each data set was processed four times with a different lookup table (LUT) to reconstruct the 32-bit floating-point data prior to the application of the transponder calibration method. It is shown that the recalibration may not be required for natural targets of relatively high HV backscattering (higher than - 26 dB), such as forests. Data recalibration improves significantly the accuracy of targets of low HV backscattering measurements, whereas like polarization (HH and VV) does not seem to be affected by the residual antenna crosstalk (-32 dB). Urban targets that manifest significant helicity scattering may have their like polarization affected and require data recalibration. However, RS2 data recalibration requires the deployment of a transponder (at HV and VH polarization) for each mode (i.e., incidence angle). Data symmetrization is introduced as a more convenient way to improve polarimetric RS2 data quality without the need to deploy transponders. It is shown that the symmetrization of the modes with the highest residual crosstalk significantly improves the calibration accuracy with a residual crosstalk lower than -37 dB. The latter is negligible as the RS2 noise floor of β <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> lies between -30 and -34 dB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.267
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2012
Admission routes2
Has abstractyes

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