Scattering type phase for wetland classification using C-band polarimetric SAR
Bibliographic record
Abstract
The Touzi decomposition is investigated for wetland characterization. Like the Cloude alpha scattering type, the magnitude alphasof the symmetric scattering is not effective for vegetation type discrimination. The phase Phialphasof the symmetric scattering type has to be used for enhanced characterization of wetland vegetation species. A new tool is introduced for assessment of the scattering type phase coherence, and the phase of the dominant scattering type is shown to be very promising for wetland target classification. The unique information provided by Phialphasfor enhanced wetland class discrimination is demonstrated using Convair-580 polarimetric C-band SAR data collected over the Mer Bleue wetland in the East of Ottawa, Canada. The use of Phialphasmakes possible the discrimination of shrub bog from sedges fen, and permits even the discrimination between conifer dominated treed bog from upland deciduous forest under leafy conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".