A Method to Mitigate the Influence of Rain on Wind Direction Estimation From X-band Marine Radar Images
Bibliographic record
Abstract
In this paper, a novel scheme is employed to mitigate the influence of rain on wind direction estimation from X-band marine radar data. Rain-contaminated regions with blurry wave signatures are first identified using the self-organizing-map (SOM)-based clustering model. Pixels located in those regions are then corrected using a deep-neural-network-based image dehazing model called DehazeNet. After obtaining the rain- corrected radar image, wind direction can be obtained using the classic curve-fitting-based method. Shipborne marine radar data collected under precipitation in a sea trial off the east coast of Canada are utilized to validate the proposed scheme. Experimental results show that the accuracy of wind direction estimation is significantly improved using the proposed scheme with a reduction of root mean square deviation (RMSD) by 19.1°.
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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 | 0.001 |
| 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) | 0.000 | 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".