Enhanced Estimation of Significant Wave Height From Rain-contaminated X-Band Radar Image Sequences
Bibliographic record
Abstract
As a result of the rain contamination on radar images, the accuracy of significant wave height (SWH) estimation from the radar images collected under rainy conditions is adversely affected. This paper presents a novel approach for enhancing SWH estimation from rain-contaminated radar images by combining DehazeNet and convolutional gated recurrent unit (CGRU) networks. Firstly, a CNN-based dehazing algorithm, i.e., DehazeNet, is employed to correct the visual degradation caused by rain in the radar images. Subsequently, the CGRU network is utilized to perform further SWH measurements from the dehazed radar images. The training and testing data used in this study were acquired by a shipborne radar in 2008 from a maritime area off the southeastern coast of Halifax, Canada. Besides, floating buoys deployed around the ship provided real-time SWH measurements, serving as the ground truth for the experiment. The experimental result demonstrates that the estimation accuracy, with a root-mean-square error (RMSE) of 0.47 m, obtained from the dehazed images surpasses the RMSE of 0.53 m achieved by applying regression networks directly to the original radar images.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".