A tensor voting approach to dark spot detection in RADARSAT-1 intensity imagery
Bibliographic record
Abstract
This paper presents a tensor voting approach to automated detection of dark spots in RADARSAT-1 ScanSAR Narrow Beam mode images. First, a thresholding algorithm that well maximizes the ratio of between-class variance to within-class variance is used to detect potential dark spot candidates. Next, a tensor voting framework integrated with sparse and dense ball votings is carried out to suppress noise while maintaining dark spots. Then, a saliency map that reflects the probability of a pixel being located within a dark spot is generated using the saliencies of ball tensors. Finally, a segmentation method is applied to ascertain dark spots based on the saliency map. The proposed approach has been tested on a set of RADARSAT-1 ScanSAR Narrow Beam intensity images. Quantitative evaluations demonstrate that the proposed approach achieves an average commission error, omission error, and quality of 0.003, 0.037, and 0.956, respectively, for detecting dark spots in SAR intensity imagery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".