Level Sets Based on AoTF Fitting Energy for Riverbank Line Extraction From SAR Images
Bibliographic record
Abstract
This article proposed a level set method (LSM) based on a novel data fitting energy for extracting riverbank lines from synthetic aperture radar (SAR) images. First, we devise a function by computing the absolute-value of the tangent function, which we call AoTF, and employ it to establish the data fitting energy. Second, the refined area fitting centers (AFCs) are developed in terms of two types of AFCs, namely average and median AFCs, which obtain better accuracy and stability. Third, a difference of Gaussian (DoG) based edge-indicator is used to supplant the Dirac-function in the model’s gradient descent flow (GDF), which encourages the evolving level sets to locate at the target edges. In addition, some regularizing constraint terms are incorporated into the objective function. Riverbank line extraction experiments on actual SAR images demonstrate that the proposed LSM outperforms some state-of-the-art methods in extraction performance and is robust to the level set initialization.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".