WSTCNN: A Wavelet Scattering Transform-CNN Model for Wind Speed Estimation From Radar Images
Bibliographic record
Abstract
Accurate estimation of ocean surface wind speed is crucial for marine meteorology, ocean engineering, and navigation safety. In this study, the WSTCNN method, which combines the wavelet scattering transform (WST) and a convolutional neural network (CNN), is proposed to estimate wind speed from X-band marine radar data. The WSTCNN method begins by applying a preprocessing technique to the raw radar images to reduce noise and enhance data quality. Then, WST is applied to the processed radar images to multi-scale, translation-invariant, and noise-robust features that reflect the patterns of wind-driven sea surface motion. These extracted features are then fed into the CNN network, which is trained to establish a mapping between the extracted features and the corresponding wind speed values. The proposed method is evaluated on two radar datasets collected under diverse conditions. The first dataset was collected using a shipborne Decca radar in an open sea region approximately 300 km off the coast of Halifax, NS, Canada, while the second was collected using a shore-based Koden radar in Guadalupe Dunes, CA, USA. Both datasets include radar data obtained in rain-free and rainy conditions, enabling a comprehensive analysis of the method’s robustness under varying environmental influences. To validate the effectiveness of the WSTCNN method, existing wind speed estimation approaches, including support vector regression (SVR) and a traditional CNN model, were applied for comparison. The results demonstrate that WSTCNN achieves superior estimation accuracy under both rainy and rain-free conditions, highlighting its robustness and adaptability across varying environmental scenarios.
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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 | 0.001 | 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) | 0.000 | 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".