Panchromatic Satellite Image Classification for Flood Hazard Assessment
Bibliographic record
Abstract
The study aims to investigate the use of panchromatic (PAN) satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood). Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following: 1) the original PAN image data, 2) the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3) the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97%) compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".