Ship Wake Detectability in TerraSAR X Imagery – Summary and Applications for Wake Detection
Bibliographic record
Abstract
Ship wakes are produced by the interaction of the ship’s hull with the ocean water and are result of multiple interacting wave systems closely beneath and on the ocean surface. The ship wake signatures in SAR imagery consists of various components. The most frequently encountered wake components are Kelvin wake arms, V-narrow wake arms and two parts of the turbulent wake: the near field and the far field. The detectability of these four most important wake components in SAR imagery is influenced by several physical variables, which are in the following called influencing parameters. The influencing parameters can be categorized into ship properties, environmental conditions and SAR acquisition settings. In a series of preceding studies of the authors, the characteristics of the effects of influencing parameters on the detectability of individual wake components have been modelled using machine learning, categorized, and contrasted against the published state-of-the-art. For the latest study of the authors, the list of the satellites was extended and the detectability of wake components was investigated in terms of different radar frequency bands (C-Band and X-Band SAR) and different orbit altitudes (i.e. slant ranges). This study summarizes the method and the results of the preceding studies and the application of the results to the actual task of wake detection is demonstrated. The demonstration shows that the developed models can be applied to control the precision performance of wake detectors and to estimate vessel velocity with an accuracy coinciding with other published methods.
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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 | 0.001 | 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".