A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning
Bibliographic record
Abstract
Wildfires negatively affect the atmosphere and ecological environment. The rapid identification of smoke is helpful for early fire detection and positioning, which are significant for fire early warning, fire point tracing, and atmospheric environment monitoring. The purpose of this research is the establishment of a smoke detection framework with which to carry out smoke identification, concentration inversion and the extraction of the smoke concentration center to realize fire source identification and positioning. The spectral characteristics and variation pattern of smoke were first studied and analyzed based on a physical correlation model and laboratory experiments. Moreover, the spectral variation of the vegetation background was measured by the Mahalanobis distance (MD), and MD-based smoke identification and concentration inversion were carried out. Then, the extraction of the smoke concentration center and fire source positioning were realized based on the Laplace operator. Finally, the application and verification of the proposed method were carried out on spaceborne data of forest smoke in Daxing’anling, China, and British Columbia, Canada. The results show that: (1) At the significance level α = 0.1%, the overall accuracy of smoke recognition based on satellite images was 91.30%, and the Kappa coefficient was 81.69%. (2) The retrieved smoke concentration was in line with the visual interpretation results. (3) The fire point location error was 23.05 ± 4.14 m (less than 2 pixels). The results indicate that the proposed MD-based smoke detection model can effectively realize smoke pixel identification and concentration inversion. The proposed smoke concentration center identification method can accurately locate the fire source and provide positioning services to trace the source of wildfires in forest fire emergencies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".