Segmentation and Classification of Polarimetric SAR Data Using Spectral Graph Partitioning
Bibliographic record
Abstract
A new approach for segmentation and classification of polarimetric synthetic aperture radar (POLSAR) data is proposed based on spectral graph partitioning. Since automated analysis techniques are often challenged due to the noisy properties of POLSAR data, human experts are employed to aid in the interpretation of such data in an operational setting. Humans can improve the performance of segmentation and classification of POLSAR data, because their vision system can apply cognitive skills that are not easy to incorporate into an automated system. The motivation for this paper is to incorporate some of these human perceptual skills into the computer algorithms. A framework that has recently emerged in computer vision for solving grouping problems with perceptually plausible results-spectral graph partitioning-is customized for POLSAR data. Segmentation is performed using the contour information in a region-based setting with the aid of spatial proximity. This is followed by a classification step performed through graph partitioning based on similarities of the mean coherence matrices obtained for each segment. Using the proposed approach, the results achieved are superior to the Wishart classifier. Automated parameter selection procedures are under development. This framework also suggests a way to accommodate different representations of polarimetric data and combine them with other information sources (e.g., optical imagery and digital elevation models).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".