Ocean Surface Wind Speed Estimation from X-Band Marine Radar Data Using Wavelet Scattering Transform-CNN
Bibliographic record
Abstract
Accurate estimation of ocean surface wind speed is critical for oceanographic applications. In this study, a wavelet scattering transform (WST)-convolutional neural network (CNN)-based method is proposed for wind speed estimation from X-band marine radar images. The proposed approach begins with applying WST to radar images to extract hierarchical and multi-scale features that capture the underlying patterns of wind-driven sea surface motion. WST effectively retains critical structural information while reducing the sensitivity to noise. The extracted features are then processed using a CNN which is trained to map these features to the corresponding wind speed values. The proposed method is validated using real radar data collected from a ship-borne Decca radar in a sea area approximately 300 km from Halifax, Canada, in 2008. Comparative experiments were conducted using a classic CNN model and the proposed WST-CNN method. The result shows that WST-CNN achieves significant improvement, with a reduction in root mean square error (RMSE) by 0.14 m/s compared to CNN. Moreover, the correlation coefficient (CC) of the proposed method reaches 0.92, demonstrating superior accuracy and robustness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".