Comparing matrix distance measures for unsupervised POLSAR data classification of sea ice based on agglomerative clustering
Bibliographic record
Abstract
Mohammed Dabboora*, John Yackelb, Mosharraf Hossainb & Alexander Braunc a Department of Geomatics Engineering, Schulich School of Engineering , University of Calgary , Calgary , Alberta , Canada , T2N 1N4 b Department of Geography , University of Calgary , Calgary , Alberta , Canada , T2N 1N4 c Department of Geosciences, School of Natural Sciences and Mathematics , The University of Texas at Dallas , Richardson , TX , 75080 , USA * E-mail: mddabboo@ucalgary.ca Clustering is a technique that can be applied for unsupervised classification of polarimetric synthetic aperture radar (POLSAR) data, an important analysis technique of radar polarimetry. Six matrix distance measures have been investigated and compared through an agglomerative clustering of RADARSAT-2 POLSAR data. The considered matrix distance measures were used as similarity criteria for merging clusters hierarchically into an appropriate number of classes. In this study, the considered distances are Manhattan, Euclidean, Bartlett, revised Wishart, Wishart test statistic, and Wishart Chernoff. Results show that the Bartlett, revised Wishart, and Wishart Chernoff distances all produce identical classification results. The Manhattan distance retrieved classification results close to those obtained by the Euclidean distance. The Bartlett, revised Wishart, and Wishart Chernoff distances produced the most accurate classification results. The study area is located in Hudson Bay offshore Churchill, Manitoba, Canada.
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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 | 0.006 |
| 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 | 0.002 | 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".