Hybrid change detection for watershed impervious surface using multi-time remotely sensed data
Bibliographic record
Abstract
In this paper, an approach to quantify basin impervious surfaces as an input variable for hydrological model was proposed, in which a hybrid change detection method and decision tree classifier based on data mining algorithm was employed using multi-temporal Landsat TM/ETM images in 1988, 1994 and 2002 at the same season in the lower reach of Yangtze River. The change types for 1994–2002 and 1988–1994 were extracted and validated with overlay analysis of GIS. The experimental results were shown that the overall classification accuracy is 88.1% compared with 69.3% of MLC in 2002 for six watershed types, and detection accuracy for five change types was 89.1% and 91.4% respectively for 1994–2002 and 1988–1994. It is demonstrated that the proposed approach is of capability for the change detecting, and can be achieved better accuracy at 30m resolution for distributed hydrological models with multi-temporal data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".