Mitigation of Rain Effect on Wave Height Measurement Using X-Band Radar Sensor
Bibliographic record
Abstract
The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height (${H}_{s}$) estimation from X-band radar sensor data along with a machine-learning (ML)-based method. First of all, the haze removal algorithm is applied to rain-contaminated radar images as pre-processing. Then, three different features are extracted from the processed radar images. Different combinations of these three features are utilized to estimate${H}_{s}$under the rain condition by using support vector regression (SVR)-based and temporal convolutional network (TCN)-based regression methods. It is found that the root-mean-square-errors (RMSEs) of${H}_{s}$estimation results using two typical methods (signal-to-noise ratio (SNR)-based and ensemble empirical mode decomposition (EEMD)-based linear fitting methods) are decreased by 0.14 m and 0.48 m after introducing the haze removal algorithm, respectively. Also, a relatively high accuracy can be achieved using the SVR-based regression method with the combination of SNR and gray level co-occurrence matrix (GLCM) features. Compared to the SNR-based and EEMD-based linear regression methods, the proposed SVR-based method further improves the estimation accuracy, with reductions of RMSE by 0.19 m and 0.82 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".