RADARSAT Constellation Mission’s Operational Polarimetric Modes: A User-Driven Radar Architecture
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".