Classifying Multi-Channel Polsar Images Base on Polarization Signature
Bibliographic record
Abstract
The conventional classifying methods use polarimetric information in the restrict number of polarization basis and frequency for polarimetric data classification. At the same time, different polarization and frequencies are sensitive to different surface scales and scattering mechanisms respectively. The combined use of different frequencies on the various polarization basis can improve the classification accuracy. This paper proposes a new classification approach for multifrequency polarimetric SAR (PolSAR) data based on polarization signature. At the first step, polarization signature is generated from coherency matrix. In the second step, the Random Forest (RF) classifier is used for classifying PolSAR data. Then, in order to combine the output of RF for incorporated three frequencies, majority voting method is used. An AIRSAR image from Sault Ste. Marie city in Ontario, Canada was chosen for this study. The results showed that different classes of land cover at different frequencies have a various performance accuracy compared to single-frequency data. Using polarization signature on three frequencies (C, L, and P) can improve classification accuracy near to 4%.
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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 | 0.002 | 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 | 0.000 |
| 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".