Historical Cumulative Change Detection in Land Cover Using Time Series PolSAR Data Based on a Difference Matrix
Bibliographic record
Abstract
For historical cumulative change detection in land cover, with the goal of addressing the inefficiencies associated with multiple paired change detections, as well as mitigating issues like false alarms and missed detections arising from the underutilization of polarization and spatial context information in previous methods, this paper introduces a time series PolSAR change detection method, which integrates the maximum eigenvalue of a difference matrix and MRF-based image segmentation using a limited amount of supervised information. The proposed method was validated using a time series dataset comprising four GF-3 PolSAR images acquired at different time points covering Wuhan City, Hubei Province, China. The experimental results indicate that the method achieves optimal performance, with Recall at 77.21, F1-score at 82.64, IOU at 70.41, and Kappa at 76.76, outperforming prior methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".