Feature extraction of dual-pol SAR imagery for sea ice image segmentation
Bibliographic record
Abstract
Dual-polarization synthetic aperture radar (SAR) image data, such as that available from RADARSAT-2, provide additional information for discriminating sea ice types compared with single-polarization data. We performed a thorough investigation of published feature extraction and fusion techniques to make optimal use of this additional information for unsupervised sea ice image segmentation. Segmentation was performed by transforming the dual-pol data (i) into a new two-channel feature space (multivariate) and (ii) into a fused single-channel feature space (univariate). Both real and synthetic dual-polarization SAR sea ice images were transformed using a variety of methods and segmented using a recognized SAR segmentation algorithm (IRGS). The results indicated that the untransformed data provides consistent and high segmentation accuracy, avoids feature extraction pre-processing, and is thus recommended for SAR sea ice image segmentation using dual-pol imagery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".