GENERATION OF CALIBRATED WIND WAVE SLOPES IMAGES FROM THE ENVISAT POLARIMETRIC DATA
Bibliographic record
Abstract
A new method for the direct measurements of an anisotropic wind induced sea waves is proposed. It exploits the polarimetric spaceborne SAR data to derive the main wave parameters. It was shown that the ratio of like-polarized and cross-polarized components of sea backscattered signal contains the direct information on slopes of wind induced waves. It is especially applicable to the high resolution SAR images because it allows to evaluate the level of waves spatial anisotropy. Also, it was shown that the radar intensities correspond to waves structure in area of atmospheric vortexes. The result had been confirmed via processing of experimental data acquired over Newfoundland, North Atlantic region from ASAR ENVISAT polarimetric data set. 1. ANALYTICAL APPROACH 1.1. Retrieving of RCS polarimetric components In this paper the simplified analysis of the polarimetric components of radar cross section (RCS) is proposed. It takes into account a spatial anisotropy of large sea waves. The analysis is based on preceding works [1,2] and regards two problems. At first, it’s an evaluation of the intensities of like-polarized and cross-polarized components (LPC and CPC), on second, it’s a possibility for measurements of anisotropic waves parameters without anyway internal or external RCS calibration, i.e. on the relation of intensities of the polarimetric components of backscattered signal. defined by accepted model of spectrum of the developed wind-induced waves. For simplification, an equation (1) was approximated by exponent, concerning to middle viewing angle θ
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".