Game Theoretic Classification of Polarimetric SAR images
Bibliographic record
Abstract
This paper reports on a region based classification of polarimetric synthetic aperture radar (PolSAR) images using the concept of game theory. The proposed method mainly contains the following steps. Firstly, the PolSAR image is partitioned into over-segments using an adaptation of k-means approach. Then, in order to compute the similarity between two distinct oversegments, a measure from polarimetric features and region size is defined. Finally, the regions or over-segments are merged into the meaningful clusters using a game theory based approach. In the game theory way, region merging problem is transformed into an iterative figure/ground separation state. In other words, considering the similarity measure, over-segments that belong to the figure compete with others through the game and obtain a considerable advantage in comparison with others. Accordingly, these privileged over-segments can be merged as an individual cluster. For clustering of the remaining over-segments, the procedure should be repeated. The performance of the proposed classification framework on simulated and real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for classification of PolSAR images.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".