Vision Transformer-Based Rainfall Detection from Nautical X-Band Radar Data
Bibliographic record
Abstract
Due to the sensitivity of the X-band signal to rain, rain may affect the precision of oceanic parameters retrieval from X-band marine radar images. In this paper, a vision transformer (ViT)-based approach is proposed to detect rain in X-band radar data, allowing the recognition of rain-contaminated radar images. Given its ability to capture long-range dependencies in images and model global context effectively, ViT is considered as a promising alternative to convolutional neural networks (CNNs) for radar image classification tasks. Each radar image is first preprocessed and then separated into patches. They are subsequently flattened and embedded to form a sequence that is supplied to the transformer encoder block. Then, the outputs from the transformer encoder are aggregated to obtain the final classification result. The data in this study were acquired using a shipborne Decca marine radar system from south-southeast of Halifax, Canada. The real-time precipitation information was provided by a Non-Acoustic Data Acquisition System (NADAS) installed on the ship. The experiment results demonstrate that the ViT-based approach achieves a relatively superior rainfall recognition precision of 98.5% compared with the support vector machine (SVM)-based approach.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".