Polarimetric Classification Using the Cloude/Pottier Decomposition
Bibliographic record
Abstract
The satellite polarimetric data does contain more information than the corresponding single polarization data. SAR polarimetry allows a discrimination of different types of scattering mechanisms because polarimetric signatures depend on the scattering process. The polarimetric classification is one of the most important applications of radar polarimetry in remote sensing. We present in this paper an unsupervised classification method that identifies target scattering characteristics. We used the entropy /alpha classification based on the Cloude /Pottier decomposition. Results are obtained using coherency matrix. Validation is done using polarimetric signature of some well known targets such as water, urban area and forests. We have two fully polarimetric images corresponding to two test sites; the first one is the Oberpfaffenhofen in Germany, provided by Aerosensing company acquired in P band and the second one is Mer bleu in Canada acquired in the C band provided by the Canadian Centre for Remote Sensing CCRS. These two zones include different themes. Results are confirmed with those published in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".