Global Significant Wave Height Retrieval from Spaceborne GNSS-R Using Transformers
Bibliographic record
Abstract
Global significant wave height (SWH) is a crucial element in ocean observation and spaceborne global navigation satellite system reflectometry (GNSS-R) stands as a novel remote sensing technique to achieve large-scale measurement. Delay Doppler Map (DDM) is a basic observable of GNSS-R and existing studies have demonstrated the effectiveness of convolutional neural networks (CNNs) in SWH retrieval from DDMs. However, CNNs are constrained by their limited receptive field, lacking the capability to establish long-range dependencies for the entire DDMs. To address this limitation, this paper proposes a novel model called WaveFormer which utilizes transformer architecture to extract features from DDMs. To evaluate the performance of the developed method, experiments are conducted on Cyclone GNSS (CYGNSS) data and results illustrate that WaveFormer achieves a lower root mean square deviation (RMSD) of 0.452 m than the CNN-based method.
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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 | 0.000 |
| 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 | 0.001 |
| 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".