Oil spill detection from dual-polarimetric Sentinel-1 SAR imagery with supervised contrastive learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing prevalence of maritime activities has heightened the risk of oil spills, necessitating robust detection mechanisms for environmental protection. Synthetic aperture radar (SAR) imagery has demonstrated significant potential in this domain due to its all-weather and day-and-night observation capabilities. However, the reliable discrimination between oil spills and look-alike phenomena remains a fundamental challenge in SAR-based detection systems. This study presents a novel end-to-end framework that incorporates supervised contrastive learning into semantic segmentation architectures to enhance oil spill detection in dual-polarimetric Sentinel-1 SAR imagery. Through comprehensive experimental validation, we demonstrate that the integration of supervised contrastive learning significantly improves the model’s capability to distinguish oil spills from look-alike phenomena, achieving substantial performance improvements in detection accuracy. The proposed methodology advances the state-of-the-art in feature representation learning for SAR-based oil spill detection, contributing to more reliable monitoring of marine environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 it