Hsu (2006), Ocean surface drift by wavelet tracking of SAR images, presented at
Bibliographic record
Abstract
Historically, ocean surface feature tracking analyses have been based on data from a single orbital sensor collected over its revisit interval of a lone, low-Earth orbital satellite. Today, ocean surface currents are being derived by performing feature tracking using data from the same type of sensors on different satellites. With all-weather, day/night imaging capability, Synthetic Aperture Radar (SAR) penetrates clouds, smoke, haze, and darkness to acquire high quality images of the Earth’s surface. The ability of a SAR to provide valuable information on the type, condition, and motion of the sea-ice, ships and surface signatures of swells, wind fronts, oil slicks, and eddies has been amply demonstrated [Liu and Wu, 2001]. This makes SAR the frequent sensor of choice for cloudy coastal regions. At present, there are three major synthetic aperture radars in orbit. Radarsat-1, the first Canadian remote sensing satellite, was launched in November 1995. Radarsat-1 has a ScanSAR mode with a 500 km wide swath and a 100 m resolution. The ERS-2, having a conventional SAR with a swath of 100 km and a resolution of 25 m, was launched in April 1995 by ESA. Envisat-1 with an Advanced SAR (either conventional narrow swath or wide swath of 405 km with 150 m resolution) was also launched in March 2002 by ESA. With repeated coverage, spaceborne SAR instruments provide the most efficient means to monitor and study the changes in important elements of the marine environment. Due to high-resolution of SAR data, the coverage of SAR sensor is always
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".