Investigation of Radarsat-2 and Terrasar-X data for river ice classification
Bibliographic record
Abstract
To date, monitoring of river ice using remote sensing has mainly focused on the use of mono-polarized and multi-polarized C-band radar data only. In this paper, Support Vector Machine (SVM) classifications using polarimetric parameters are tested to identify types of river ice. Classification algorithms are validated on the newly available C-band Radarsat-2 and X-band Terrasar-X data to investigate the potential of this new imagery, acquired in winter 2009. An electromagnetic model is improved to simulate the polarimet-ric response of a river ice cover to understand the interactions of the radar signal with the ice cover. At C-band, using dual-polarized data over mono-polarized data increases by 23.9% the final classification producer accuracy. Furthermore, the best producer accuracy is 91.6% when using dual-pol data at C-band, which stand for a gain of 2.2% compared to dual-pol data at X-band.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".