A New Algorithm for Significant Wave Height Estimation From X-Band Radar Images
Bibliographic record
Abstract
Accurate estimation of significant wave height (SWH) is critical for understanding ocean surface dynamics and supporting marine operations. In this study, a novel approach combining wavelet scattering transform (WST) and convolutional neural network (CNN), termed WST-CNN, is proposed for SWH estimation using X -band marine radar images. The hierarchical and multi-scale features extracted by WST effectively capture the intricate patterns of ocean surface motion while being robust to noise and translation invariance. Then, these features are processed by a CNN to map the radar data to corresponding SWH values. The radar data in this work were collected in 2008 from a ship-mounted Decca radar operating in a sea area approximately 300 kilometers off the coast of Halifax, Canada. Ground truth SWH values were measured by buoys deployed around the data collection vessel. The CNN method is also applied to the radar data and used for comparison with the proposed method. Results show that the WST-CNN method achieves a better accuracy, with a root mean square error (RMSE) reduced by 0.03 m and bias decreased to 0.01 m compared to CNN before averaging. Additionally, the proposed WST-CNN method achieves an RMSE reduced to 0.15 m after averaging, along with an improved correlation coefficient (CC) of 0.98.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".