SWHFormer: A Vision Transformer for Significant Wave Height Estimation From Nautical Radar Images
Bibliographic record
Abstract
This paper presented a novel significant wave height (SWH) estimation method, SWHFormer, which incorporates the Vision Transformer (ViT) to estimate SWH from X-band nautical radar images. Unlike traditional convolutional neural networks, the ViT model treats the input as a sequence, capitalizing on its attention mechanism to capture long-range dependencies, resulting in superior performance in capturing the complex patterns present in sea wave dynamics. The radar data undergo an image denoising routine, followed by patching, flattening, and embedding processes to form a sequence fed into the Transformer encoding module. The outputs from the encoder are then aggregated to derive the final regression result, i.e., SWH estimation. In order to evaluate the performance of SWHFormer, the dataset collected by a Decca radar aboard a free-navigating vessel is analyzed, both buoy and model-based data are utilized as ground truth. In this study, two traditional linear fitting methods, i.e., ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD)-based approaches, and a recent deep learning algorithm, convolutional gated recurrent unit (CGRU) network are exploited for comparison with SWHFormer. It is found that the root mean square error (RMSE) of the estimated results using the proposed SWHFormer is decreased from 0.29 m, 0.26 m, and 0.18 m to 0.16 m after the temporal moving average, respectively, compared to the above three methods, when the buoy-measured SWH is served as ground truth. Besides, it is decreased from 0.30 m, 0.28 m, 0.16 m to 0.14 m, respectively, when the model-based SWH is employed as reference.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".