MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation
Bibliographic record
Abstract
Sea ice segmentation from synthetic aperture radar (SAR) imagery is a key task in polar sea ice monitoring, which is crucial for global climate prediction and polar route planning. However, the existing sea ice segmentation algorithms for SAR images often fail to consider long-range contextual dependencies when capturing multiscale features, resulting in an inability to fully exploit multiscale global contextual information. To address this limitation, we proposed a novel encoder–decoder structure network for multitask sea ice segmentation. Initially, a cross-scale interaction module was constructed in the encoder that utilizes cross attention to seamlessly capture multiscale features, effectively bridging the semantic information gap between different layers. Subsequently, a context transformer block based on efficient multihead self-attention was developed to model remote dependencies across spatial and channel dimensions, thereby enhancing the extraction of multiscale global contextual information. Furthermore, a channel patch module was introduced that allows for the strategic refinement of differential features to emphasize changing areas and suppress artifacts. In the final stages, a refined multiscale feature fusion module was embedded in the decoder to strategically integrate the feature maps generated, thus iteratively merging layered features for enhanced segmentation. Our experiments on the AI4Arctic Sea Ice Challenge Dataset show that MFGC-Net achieves outstanding performance in multitask sea ice segmentation compared with current state-of-the-art methods, as demonstrated by both quantitative and qualitative results.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 | 0.000 |
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".