Comparison and fusion of co‐occurrence, Gabor and MRF texture features for classification of SAR sea‐ice imagery
Bibliographic record
Abstract
Image texture interpretation is an important aspect of the computer‐assisted discrimination of Synthetic Aperture Radar (SAR) sea‐ice imagery. Co‐occurrence probabilities are the most common approach used to solve this problem. However, other texture feature extraction methods exist that have not been fully studied for their ability to interpret SAR sea‐ice imagery. Gabor filters and Markov random fields (MRF) are two such methods considered here. Classification and significance level testing shows that co‐occurrence probabilities classify the data with the highest accuracy, with Gaborfilters a close second. MRF results significantly lag Gabor and co‐occurrence results. However, the MRF features are uncorrelated with respect to co‐occurrence and Gabor features. The fused co‐occurrence/MRF feature set achieves higher performance. In addition, it is demonstrated that uniform quantization is a preferred quantization method compared to histogram equalization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".