A semi-supervised approach for ice-water classification using dual-polarization SAR satellite imagery
Bibliographic record
Abstract
The daily interpretation of SAR sea ice imagery is very important for ship navigation and climate monitoring. Currently, the interpretation is still performed manually by ice analysts due to the complexity of data and the difficulty of creating fine-level ground truth. To overcome these problems, a semi-supervised approach for ice-water classification based on self-training is presented. The proposed algorithm integrates the spatial context model, region merging, and the self-training technique into a single framework. The backscatter intensity, texture, and edge strength features are incorporated in a CRF model using multi-modality Gaussian model as its unary classifier. Region merging is used to build a hierarchical data-adaptive structure to make the inference more efficient. Self-training is concatenated with region merging, so that the spatial location information of the original training samples can be used. Our algorithm has been tested on a large-scale RADARSAT-2 dual-polarization dataset over the Beaufort and Chukchi sea, and the classification results are significantly better than the supervised methods without self-training.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".