Improved Sea Ice Concentration Estimation Through Fusing Classified SAR Imagery and AMSR-E Data
Bibliographic record
Abstract
. A method to automatically combine binary ice/water information from synthetic aperture radar (SAR) sea ice images with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) daily ice concentration product is proposed for the purpose of generating sea ice concentration estimates with improved detail and accuracy. First, each pixel in the SAR image is labeled as ice or water using the MAp-Guided Ice Classification (MAGIC) SAR image classification system. Second, the labeled pixels are modeled as a Bernoulli process and combined with the AMSR-E ice concentration data in a Bayesian framework to generate improved ice concentration estimation. Visually interpreted ice/water extent and sea ice image analyses from the Canadian Ice Service (CIS) are used as comparison data. The combination of SAR ice/water labeled pixels with the AMSR-E ice concentration is shown to improve the ice concentration estimates, especially at the ice edge where substantial improvements are observed. Although the present study uses ice/water information from SAR, the method is general and could be used with other sources of ice/water remote sensed data.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".