Segmentation of polarimetric SAR data using contour information via spectral graph partitioning
Bibliographic record
Abstract
A new method for segmenting polarimetric Synthetic Aperture Radar (POLSAR) data is proposed. Image segmentation is formulated as a graph partitioning problem. Spectral graph partitioning - known to provide perceptually plausible image segmentation results using one or more cues (e.g., similarity, proximity, contour continuity) - is applied on POLSAR image data. The degree of similarities between pairs of pixels are calculated based on contour information. Graph partitioning is performed using the Multiclass Spectral Clustering method that minimizes the normalized cut cost function to ensure minimal similarity between partitions. The resulting segmentation is an approximation to the global optimal solution. C-band POLSAR data acquired by CV-580 are used for testing the performance. The results are found to closely agree with manual segmentations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".