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Record W3158147623 · doi:10.1080/07038992.2021.1907566

RADARSAT Constellation Mission’s Operational Polarimetric Modes: A User-Driven Radar Architecture

2021· article· en· W3158147623 on OpenAlexafffundvenueabout
R. K. Raney, Brian Brisco, Mohammed Dabboor, Masoud Mahdianpari

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2021
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsMemorial University of NewfoundlandCentre For Cold Ocean Resources EngineeringEnvironment and Climate Change Canada
FundersNatural Resources Canada
KeywordsPolarimetryConstellationRemote sensingStokes parametersRadarComputer sciencePolarimeterSynthetic aperture radarScatteringOpticsGeographyPhysicsTelecommunicationsAstronomy

Abstract

fetched live from OpenAlex

Canada’s Earth-observing RADARSAT Constellation Mission (RCM) is intended to serve operational users. The users’ main objectives were to have routinely available high-quality quantitative information about their applications, with large area coverage potential. That two-part requirement was sufficient to establish an innovative synthetic aperture radar (SAR) polarimeter’s end-to-end system profile, the hybrid compact polarimetric (HCP) architecture. HCP’s essential and defining characteristic is circularly polarized transmission. This is sufficient to evaluate the backscatterer Stokes vector, but only half of the scattering matrix elements are measured. Hence image classification methodologies for linearly polarized full- (or quad-) pol (FP) radars that depend on knowledge of all four of the scattering matrix elements if applied to HCP-derived data lead to erroneous results. HCP-appropriate classifications are based on the Stokes vector. Related methods traditionally used for radar astronomy—for which circularly polarized transmission is the norm—are reviewed. Those known methods are extended, bringing to light fundamental characteristics of a polarimetric electromagnetic field. Analysis tools appropriate for HCP’s polarimetric data are introduced. The resulting polarimetric portraits—defined as the Stokes vector of the backscattered field in response to balanced illumination of the scene—from FP and HCP polarimeters are shown to be equivalent.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.993
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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
GenreMethods

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

Citations29
Published2021
Admission routes4
Has abstractyes

Explore more

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