Ship Detection Performance Predictions for Next Generation Spaceborne Synthetic Aperture Radars
Bibliographic record
Abstract
Following success in other areas as a remote sensor, the spaceborne microwave image radars are assuming a notable position in the problem of ship detection for civilian and military purposes. This work will discuss the strong and weak points of Synthetic Aperture Radar (SAR) when used for ship detection. First, the thesis gives a brief description of SAR fundamentals, image processing and the parameters for ship detection. Second, the actual techniques, limitations, errors and some models used for ship detection are described. Finally, using a well-known and reliable ship detection model (Vachon et al. 1997), tested in the Canadian Ocean Monitoring Workstation and in some validation field programs, the new generation of spaceborne SARs, mainly RADARSAT 2, are analyzed for ship detection capabilities. During the analysis parameters like wind velocity, wind direction related to the antenna, satellite incident angle and Number of Looks are changed to study their influence on ship detection.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".