Sea Surface Wave Height Estimation and Improvement from Rain-Contaminated X-Band Nautical Radar Data
Bibliographic record
Abstract
In this work, an improvement for the quality of X-band radar images affected by rain is proposed, and a support vector regression (SVR)-based method is designed to further obtain the significant wave height (Hs). The first step is to implement the dehazing algorithm on the radar images influenced by rain. Then, SVR is employed to train the Hsregression model including two features, i.e., gray level co-occurrence matrix (GLCM) and signal-to-noise ratio (SNR) features, extracted from rainless data. Finally, Hscan be obtained from the trained model with these two features extracted from the dehazed images. Besides, two classical Hsestimation methods, i.e., ensemble empirical mode decomposition (EEMD)-and SNR-based regression algorithms are utilized for analyzing the effectiveness of the dehazing algorithm. Experiment results confirm that dehazing algorithm can substantially decrease the root-mean-square-error (RMSE) and biases of the estimated Hsand increase the correlation coefficients (CCs) between the results and buoy data. Furthermore, comparisons with the SNR- and EEMD-based regression algorithms incorporating the dehazing algorithm illustrate that RMSEs obtained from the proposed method are further reduced to 0.44 m from 0.49 m and 0.78 m, respectively.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".