A CNN-based Hybrid Dehazing and Regression Model for Sea Surface Wind Speed Retrieval from Rain-contaminated Marine Radar Data
Bibliographic record
Abstract
In recent years, the widespread application of X-band radar for measuring ocean surface parameters has become common, yet the presence of rain significantly hinders the marine radar system. As a result, this paper introduces a model that combines the dehazing technique with the convolutional neural network (CNN) regression network to estimate sea surface wind speed from X-band radar images collected under rainy conditions. Specifically, the CNN-based end-to-end dehazing system is first applied to the rain-contaminated radar images to mitigate the blurred areas caused by rain. Then, a deep CNN regression network is utilized to estimate the wind speed. The radar data were collected from a shipborne Decca radar in a sea area 300 km from Halifax, Canada, in 2008. In contrast to the wind speed estimation method based on support vector regression (SVR), the utilization of the CNN network demonstrates superior accuracy in wind speed measurement, yielding more precise results. The root-mean-square error (RMSE) of the estimated result from the Decca radar data is decreased to 1.02 m/s from 1.24 m/s, and the correlation coefficient (CC) is increased to 0.95 from 0.91.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".