Wave Height Estimation from X-Band Marine Radar Data Using SWHFormer Method
Bibliographic record
Abstract
This paper presents an innovative deep learning-based approach to measuring sea significant wave height (SWH) from shore-based X-band radar data. Initially, the first image from each minute of the radar sequence acquired by the X-band radar is extracted and sub-images covering the ocean area are intercepted for analysis. Subsequently, these extracted radar sub-images are fed into a SWH regression network based on the Vision Transformer (ViT) model, named SWHFormer, for training the model and conducting real-time SWH measurements. The radar data used in the validation experiment were collected at Guadalupe Dunes, CA, USA. Concurrent hourly SWH data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are employed as ground truth. Comparative analysis with several existing SWH estimation methods reveals that the proposed SWHFormer-based method achieves a relatively superior SWH estimation performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".