Fully Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-Spot Detection In SAR Imagery
Bibliographic record
Abstract
Dark-spot detection from synthetic aperture radar (SAR) imagery is a fundamental step in marine oil-spill detection and monitoring. However, achieving robust and accurate detection is difficult due to SAR sensor limitations and the complex marine environment. To address this problem, the large-scale spatial contextual information in SAR imagery has to be utilized to increase the class separability between the dark spot and the background. A stochastic fully connected continuous conditional random field (SFCCRF) approach to model SAR imagery and perform soft-label inference has been designed and built, leading to an efficient detection algorithm. Instead of treating all pixels in the imagery as being connected, SFCCRF determines the connectivity of two pixels in a stochastic manner based on their proximity in both feature space and image space. Since SFCCRF provides an efficient and effective way for modeling the large-scale spatial correlation effect, the resulting soft labels can resist the influence of speckle noise and highlight the difference between dark spot and the background. Dark-spot detection is achieved by binarizing the soft labels estimated by SFCCRF. The proposed algorithm is tested on both simulated and real SAR imageries. The results show that SFCCRF can delineate the dark spot with low commission and omission error rates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".