A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping
Bibliographic record
Abstract
Change detection using Remote Sensing (RS) techniques is valuable in numerous applications, including environmental management and hazard monitoring. Synthetic Aperture Radar (SAR) images have proven to be even more effective in this regard because of their all-weather, day and night acquisition capabilities. In this study, a polarimetric index based on the ratio of span (total power) values was introduced, in which neighbourhood information was considered. The role of the central pixel and its neighbourhood was adjusted using a weight parameter. The proposed index was applied to detect flooded areas in Dongting Lake, Hunan, China, and was then compared with the Wishart Maximum Likelihood Ratio (MLR) test. Results demonstrated that although the proposed index and the Wishart MLR test yielded similar accuracies (accuracy of 94% and 93%, and Kappa Coefficients of 0.82 and 0.86, respectively), inclusion of neighbourhood information in the proposed index not only increased the connectedness and decreased the noise associated with the objects within the produced map, but also increased the consistency and confidence of the results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".