Unsupervised Segmentation of Multilook Compact Polarimetric Sar Data based on Complex Wishart Distribution
Bibliographic record
Abstract
The Canadian RADARSAT Constellation Mission (RCM) proposes a new synthetic aperture radar (SAR) data mode called compact (hybrid or partial) polarimetry (CP) in a wide swath. Compact polarimetry maximizes the measurement potential if the multilook complex (MLC) coherence matrix of the SAR backscattered field is used. The MLC CP coherence matrix follows the Wishart distribution. In this paper, an unsupervised region-based semantic segmentation of the MLC CP coherence matrix data using the complex Wishart distribution is presented. The segmentation method is an extension of the iterative region growing with semantics (IRGS) to complex CP data. The proposed algorithm is called CP-IRGS and is formulated based on conditional random fields (CRFs) incorporating edge strength over the image. Applications of the algorithm are demonstrated using a simulated MLC CP data set and a real single-look complex (SLC) quadrature polarimetric (QP) SAR data set which is used to derive the MLC CP 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".