Enhancement of doppler centroid for ocean surface current retrieval from ERS-1/2 raw SAR
Bibliographic record
Abstract
Ocean surface current information is one of the important factors which are employed for a variety of scientific pursuits especially on ocean environment. Although remote sensing techniques have been developed up to now, the investigation of ocean surface current using synthetic aperture radar (SAR) is not easy of access. This paper presents the results of ocean current observation using ERS-1 raw SAR data which were obtained off the coast of Jeju Island. We extract the ocean current based on the concept in which Doppler frequency shift and the ocean current are closely related. Moving targets cause Doppler frequency shift of the backscattered radar radiation of SAR, thus the line-of-sight velocity of the scatters can be evaluated. The Doppler frequency shift can be measured by estimating the difference between Doppler centroid obtained and reference Doppler centroid calculated. Theoretically, the Doppler centroid is zero, however, squinted antenna which is affected by several physical factors causes Doppler centroid to be nonzero. The Doppler centroid can be estimated from measurements of sensor trajectory, attitude and Earth model. By compensating ERS attitude errors, we could enhance Doppler centroid accuracy and verify that the extracted ocean surface current is more coincident with the in-situ data. We present here the results of estimated ocean surface current and observed in-situ data, which are in agreement within the limit of error bounds
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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 | 0.001 | 0.002 |
| 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".