Fine resolution classification of new ice, young ice, and first-year ice based on feature selection from Gaofen-3 quad-polarization SAR
Bibliographic record
Abstract
Sea ice in the high latitude is an indicator of climate change and has undergone dramatic changes because of recent global warming. Synthetic aperture radar (SAR) is a relatively practical tool for sea ice monitoring because of its low sensitivity to clouds, rain, and fog, as well as its capability for high-resolution earth observation in daylight or darkness. With the progression in SAR systems from single-pol to dual-pol, quad-pol and hybrid-pol, large numbers of parameters have been proposed for sea ice classification. Even though a large number of SAR characteristics have been used to classify sea ice, it remains unclear which parameters are the most effective for different regions and seasonal or environmental conditions. Meanwhile, classification studies for fine sea ice with high spatial resolution and many sub-types of sea ice, particularly in the case of rapidly changing first-year ice (FYI), which includes new ice (NI), young ice (YI), and FYI, are rather few. NI and YI have comparatively thinner thickness, and are often classified as FYI in these studies[1].A new method of sea ice classification based on feature selection from Gaofen-3 polarimetric SAR observations is proposed. The new approach classifies sea ice into four categories: open water, NI, YI, and FYI. Seventy parameters that have previously been applied to sea ice studies are re-examined for sea ice classification in the Okhotsk Sea near the melting point on 28 February 2020. The ‘separability index’ is used for the selection of optimal features for sea ice classification. Full polarization (σohh, SEi, Ks) and hybrid polarization parameters (σorl, CPSEi,ρrh-rv, αs) are determined as optimal. The selected parameters are used to classify NI, YI, and FYI using a SVM machine learning classifier; and classification results are validated by manually interpreted ice maps derived from Landsat-8 data. [1]Sea ice: types and forms - Canada.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".