Unsupervised Detection for Burned Area with Fuzzy C-Means and D-S Evidence Theory
Bibliographic record
Abstract
Mapping the burned area of forest fires can contribute significantly to the understanding, quantification, and evaluation of forest fire severity and its impacts on the forest ecosystem. In this paper, an unsupervised detection for burned region based on the Fuzzy C-Means (FCM) and Dempster-Shafer (D-S) evidence theory with the bi-temporal images is proposed. Specifically, according to difference maps from the delta normalized burn ratio and spectral angle index, the Expectation-Maximization (EM) algorithm is used to separate the study area into the definitely burned region and indefinitely burned region. Then, under the enlightenment of the multi-source information fusion theory, the indefinite region is discriminated against further with FCM and D-S evidence theory. Finally, the final fire-burned map can be inferred from the results obtained from the aforementioned steps. The experimental results on two forest fires with bi-temporal Landsat-8 images have shown the potential of the proposed burned area mapping method, in the field of detecting the forest landscape change based on multispectral remote sensing images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 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".