Adaptive multiresolution quantization for contextual information gain in SAR sea ice images
Bibliographic record
Abstract
The authors describe an adaptive multiresolution technique that quantizes SAR sea ice images to improve contextual information such as the spatial, relational make up of ice types in a region. First, they use dynamic local thresholding to extract regional intensity threshold values from which a histogram is constructed. Next, they blur the threshold histogram with varying window sizes to build a multiresolution contour map. They identify peaks based on a cumulative distribution function and track each peak on the contour map to assess its significance. Then, for each significant peak identified, they cluster the threshold values extracted during dynamic local thresholding using nearest-neighbors to establish sets of threshold values. Finally, they assign a pixel its quantization value by comparing its original intensity to the set that it belongs to. The technique handles noise and preserves contexts, ensuring a consistent and smooth quantization of the image. They have applied the technique to a large number of ERS-1, ERS-2, and RADARSAT images to obtain quantized representation of the images for contextual information gain. They have also embedded the technique in an unsupervised sea ice segmentation tool that has been installed at the National Ice Center and the Canadian Ice Service.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".