A tool for semi-automated extraction of waterbody feature in SAR imagery
Bibliographic record
Abstract
This letter describes the mechanisms of a semi-automated approach of waterbody (lakes and watercourses) feature extraction in synthetic aperture radar (SAR) imagery. The approach is semi-automatic because it requires an interest region for each waterbody to be extracted. This interest region can be provided by the user (manually drawn in the case of new feature extraction) or imported from an existing spatial database (on the case of maps updating). Once the interest region is determined, the tool produces the waterbody feature then the user rejects, accepts after amending or accepts it directly. To process a waterbody, the procedure occurs in four main steps: (1) interest region delimitation; (2) estimation of statistical characteristics of the two regions: inside waterbody and outside waterbody; (3) classification of each resolution cell as inside waterbody or as outside waterbody and (4) determination of main waterbody and converting its edge to a feature. The later will be indexed in a spatial database. The approach accelerates and ameliorates waterbody feature extraction, and it has been tested with success and then integrated into a topographic map production system, especially for the Canadian northern regions, where there is a high density of waterbodies and where Radarsat-2 satellite (our provider of SAR images) are regularly used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".