Semi-Supervised Sea Ice Classification of SAR Imagery Based on Graph Convolutional Network
Bibliographic record
Abstract
Monitoring sea ice in polar regions is essential for environmental modeling and ship navigation. National ice agencies expect robust sea ice classification methods for operational use. However, fully supervised machine learning models require large training datasets, which are usually limited to the sea ice classification domain. Therefore, a semi-supervised sea ice classification model is proposed to address this challenge. First, the IRGS segmentation is applied to generate superpixels that construct the graph. Then, two graph convolutional layers are utilized to learn the features of each node. Finally, a softmax layer assigns labels to the nodes in the graph. The proposed model is named IRGS-GCN and tested on four RADARSAR-2 dual-polarized scenes. The experimental results show that the IRGS-GCN achieves an overall accuracy of 95.17% and outperforms fully-supervised random foreset and ResNet trained on limited data. Most of the sea ice boundary and leads are successfully preserved in the results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".