Oil spill candidate detection from SAR imagery using a thresholding-guided stochastic fully-connected conditional random field model
Bibliographic record
Abstract
The detection of marine oil spill candidate from synthetic aperture radar (SAR) images is largely hampered by SAR speckle noise and the complex marine environment. In this paper, we develop a thresholding-guided stochastic fully-connected conditional random field (TGSFCRF) model for inferring the binary label from SAR imagery. First, an intensity thresholding approach is used to estimate the initial labels of oil spill candidates and the background. Second, a Gaussian mixture model (GMM) is trained using all the pixels based on the initial labels. Last, based on the GMM model, a graph-cut optimization approach is used for inferring the final labels. By using a threholding-guided approach, TGSFCRF can exploit the statistical characteristics of the two classes for better label inference. Moreover, by using a stochastic clique approach, TGSFCRF efficiently addresses the global-scale spatial correlation effect, and thereby can better resist the influence of SAR speckle noise and background heterogeneity. Experimental results on RADARSAT-1 ScanSAR imagery demonstrate that TGSFCRF can accurately delineate oil spill candidates without committing too much false alarms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".