Retrieval of ocean wave spectra and RAR MTFs from dual-polarization SAR data
Bibliographic record
Abstract
A method for the retrieval of the real aperture radar (RAR) modulation transfer function (MTF) and ocean wave spectra from dual-polarization (i.e. simultaneously acquired HH and VV polarizations) synthetic aperture radar (SAR) image data is described. The RAR MTF is estimated by applying empirical MTF estimation methodologies to inter-look cross spectra between various combinations of individual looks and available polarizations for a given radar frequency. The concept behind the non-linear inversion is that any combination of like- and cross-polarization image spectra should return the same wave spectrum, in agreement with in situ and model wave spectra. This permits estimation of the RAR MTF, which are shown to be inadequate for the range of conditions encountered in our data set. However, the theory and measurements fit well in describing the polarization dependence of the RAR MTF. The data set consists of SIR-C/X-SAR, L-band and CCRS CV-580 C-band SAR data, in situ buoy measurements, and model data from field programs in Canadian waters in October and December 1994.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".