Mapping forest fire scars with simulated RCM compact-pol data
Bibliographic record
Abstract
This paper gives results of our assessment of the potential for using Radarsat Constellation Mission (RCM) C-band compact polarimetry (compact-pol) for detecting historical forest fire scars. We first summarize the compact-pol decomposition theory we developed for retrieving useful geophysical parameters from compact-pol data. We then demonstrate a combination of time series filtering and spatial filtering to reduce speckle noise in the geophysical parameters. Next we describe a rule-based classifier and show an application example based on a time series of simulated compact-pol data from Radarsat-2 Fine Quad-pol (FQ) mode to detect a 10-year old fire scar in our study site. Our study results showed that even though there was a loss of polarimetric information through projection of a complex scattering matrix of quad-pol data on a single-pixel level, the compact-pol mode was capable of maintaining important polarimetric information and detecting the test forest fire scar. Finally we look at the effect of non-circular transmit polarization on key decomposition parameters and discuss the effects of imperfect transmit polarization on classification performance.
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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 | 0.002 |
| 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".