Towards an automated ocean feature detection, extraction and classification scheme for SAR imagery
Bibliographic record
Abstract
Spaceborne synthetic aperture radar (SAR) observation is an important tool for monitoring and studying changes in various geophysical elements in and above world oceans. Because of SAR's ideal imaging capability and high resolution, the collection of SAR data will likely extend well into the 21st century. As the data become increasingly abundant and computers faster and more affordable, it naturally leads to an increasing need for an automated procedure to replace the labour-intensive manual screening process. In this paper, an integrated scheme for detection, extraction and classification of linear ocean features in SAR imagery is attempted for the purpose of automated screening. The methodology consists of feature detection based on greyscale histogram screening, feature extraction based on two-dimensional wavelet analysis and feature classification based on texture analysis. Using these algorithms on SAR data, several case studies of linear ocean features, including fronts, ice edges and a polar low, are presented herein. Though not fully automated at this stage, the integration of these algorithms seems to lay a promising foundation for the future development of a more automated ocean feature detection, extraction and classification scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".