A Salient Region Detection and Pattern Matching-Based Algorithm for Center Detection of a Partially Covered Tropical Cyclone in a SAR Image
Bibliographic record
Abstract
Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution, large area coverage, day/night imaging capability, and penetrating cloud capability, has been used as an important tool for tropical cyclone monitoring. The accuracy of locating tropical cyclone centers has a large impact on the accuracy of tropical cyclone track prediction. Usually, the center of a tropical cyclone can be accurately located if the tropical cyclone eye is fully covered by a SAR image. In some cases, due to the limited coverage of the SAR, only a part of a tropical cyclone can be imaged without the eye. From a SAR image processing point of view, these facts make the automatic center location of tropical cyclones a challenging work. This paper addresses the problem by proposing a semiautomatic center location method based on salient region detection and pattern matching. A salient region detection algorithm is proposed, in which the salient region map contains mainly the rain bands of a tropical cyclone in a SAR image. The pattern matching problem is transformed into an optimization problem solved by using the particle swarm optimization algorithm to search the best estimated center of a tropical cyclone. To estimate the accuracy of the located center, we compare the results with the NOAA National Hurricane Center's best track data. Experiments demonstrate that the proposed method achieves good accuracy for locating the centers of tropical cyclones from SAR images that do not contain a distinguishable eye signature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".