Application of Gaussian Markov random field model to unsupervised classification in polarimetric SAR image
Bibliographic record
Abstract
The aim of this paper is to demonstrate that the Gaussian Markov random field (GMRF) model can be successfully applied to the classification pf multi-frequency polarimetric SAR data. As a special case of MRF, the GMRF has been shown to be an accurate compact representation from a single-band textured images or multi-band textured images. To apply the method to the classification of inter-channel correlated polarimetric SAR data, we first transformed the data into combination of uncorrelated principal component images. Both intensities (hh, hv, and vv) and phase difference (/spl phi//sub hh - vv/) images of L- and P-band data are considered for classification in the study area in Jeju Island, South Korea. The properties of the transformed data reveal that the images tend to be Gaussian and they are mutually uncorrelated. The GMRF model therefore can be applied to the classification of the transformed polarimetric SAR data. As the GMRF model is a type of classifier based on segment merging, the classification process begins from the initial guess consisting of large amounts of segments. Spatially and statistically similar regions are combined to update the segmented map for each iteration. The final classification map based on polarimetric characteristics shows improvements in the accuracy and efficiency of the classification frame for the tested polarimetric SAR data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".