Consistency Adjustment of Time-Series Land Cover Products Based on Matrix Decomposition
Bibliographic record
Abstract
In recent years, the continuous increase in the number of earth observation satellites has led to the development of time-series land cover products. To obtain a land cover map with better spatiotemporal consistency and classification accuracy, this paper proposes a time series land cover map consistency adjustment method based on matrix decomposition. Matrix decomposition refers to the decomposition of a matrix into the product of several matrices, whose sizes are usually simpler than the original matrix and can represent the features of the original matrix. To verify the effectiveness of this method, Gaofen-1 satellite time series images of Shunyi District, Beijing were used for consistency adjustment. The final results show that the overall accuracy of the confusion matrix of the SVM (Support Vector Machine) classification results is 85.3952% while the overall accuracy of the confusion matrix increases to 90.3164% after consistency adjustment, and the Kappa coefficient is 0.8537. The accuracy of land cover maps and the consistency between different times of maps have been significantly improved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".