Measuring Synthetic Aperture Radar target differences with stochastic distances
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) imagery plays a central role as a source of unique data for Geographic Information Systems. These data sets provide complementary information to that provided by optical and infra-red sensors as, for instance, Landsat TM, CBERS-2, IKONOS and SPOT, to name a few. SAR sensors capture information about the target roughness and its dielectric properties, and their imaging capabilities are able to penetrate clouds, fog, rain and even some types of land cover as, for instance, forest canopies. A major issue related to the use of SAR images is their statistical behavior. It is well known that classical Gaussian and additive models do not hold for such data. The multiplicative model (MM) has been extensively tested with success, and it is able to explain phenomenological aspects of the image formation. One of the most important distributions related to the MM is the G° law. The G° distribution, as all other laws related to the MM, greatly departs from the Gaussian model. This paper assesses the SAR image discrimination capabilities of selected parametric methods based on divergences measures, when compared to the nonparametric Kolmogorov-Smirnov testing methodology. The importance of the Triangular and Arithmetic-Geometric distances is quantified with respect to the Kullback-Leibler parametric and Kolmogorov-Smirnov non-parametric classical distances by means of Monte Carlo simulation.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".