Theory and application of deorientation for target scattering Part II: application to terrain surface classification
Bibliographic record
Abstract
In the Part I. the deorientation theory and algorithm of target polarimetric scattering has been illlustrated. Through transformation of target scattering vector, the descript meanings of Ψ,u,ν and H are analyzed. In Part II, a vector radiative transfer (VRT) model for natural terrain surface is adopted to simulate and analyze the capabilities of each parameter in terrain surface classification. It concludes that H indicates the complexity of stratified terrain canopy; ν indicates different scattering mechanisms; u indicates various targets' properties; Ψ indicates the orientation state of target. Then an unsupervised classification scheme is developed based on(u,ν,Ψ)-H , which firstly classifies terrain surface into different classes by u,ν,H, and secondly analyzes the orientation distribution of each class by Ψ. As examples, a SIR-C polarimetric image over China's Guangdong Hui-Yang district is classified and a AirSAR polarimetric image over Canada's Boreal district is orientation-analyzed.
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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 | 0.002 | 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 | 0.000 |
| 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 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".