A semisupervised contextual classification algorithm for multitemporal polarimetric SAR data
Bibliographic record
Abstract
This paper presents a contextual classification algorithm which employs the multiscale modified Pappas adaptive clustering (MPAC) approach and the Semisupervised Expectation-Maximization (SEM) procedure for urban land cover mapping using multitemporal polarimetric SAR (PolSAR) data. The proposed pixel-based algorithm explores spatio-temporal contextual information and thus could effectively improve the classification accuracy while simultaneously avoids the pepper-salt results which often occurs on the SAR images. Moreover, owing to the multiscale analysis, MPAC could adaptively preserve the detailed features comparing with other non-adaptive contextual methods. The proposed algorithm is computationally efficient and requires less parameter to be estimated. Properties of the proposed algorithm including the MRF impact, multiscale efficiency, computational performance and the initialization influence were investigated. Six-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used for validation. The results show that this algorithm could generate homogenous and detailed mapping results with fair accuracy for complex urban land cover classification.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".