Multi-scale feature tracking in sequential satellite images with wavelet analysis to measure sea surface currents in the Gulf of St. Lawrence
Bibliographic record
Abstract
Measuring sea surface currents is a technological challenge in oceanography. Feature tracking in time series of remote sensing imagery has been proposed as a way to address this problem. The most commonly used approach is the maximum cross-correlation (MCC) method, originally developed to track cloud motion. We propose a new technique that makes use of Daubechies wavelet analysis combined with the MCC method. In our approach, satellite images are decomposed into various spatial scales using the wavelet transform, and the location with the MCC coefficient among all the scales is selected as the most likely new position of the tracked feature. Results from the analysis of five pairs of sequential National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) images of the Gulf of St. Lawrence area show that wavelet analysis improves the estimated sea surface current field by increasing the number of current vectors about 20% under the same confidence level (0.9) as compared with that using the MCC method alone.
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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 | 0.001 |
| 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) | 0.000 | 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".