Automated Detection of Icebergs in the North Atlantic Using RCM Dual-Polarimetric SAR Imagery for Offshore Safety
Bibliographic record
Abstract
Abstract Icebergs impose a significant threat to shipping, offshore oil operations, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean is particularly challenging due to frequent cloud cover. Synthetic Aperture Radar (SAR) has emerged as an effective solution for addressing these challenges. This study presents a novel method for detecting icebergs under varying sea conditions using C-band dual-polarimetric imagery from the RADARSAT Constellation Mission (RCM) using the data collected along Canada’s east coast during iceberg-prone seasons in 2022 and 2023. Our approach processes large SAR images by dividing them into 100 x 100-pixel patches, each covering an area of 5 km x 5 km, and identifies icebergs within each patch. The method combines statistical features, which highlight subtle patterns in RCM imagery, with high-dimensional features extracted from three pre-trained convolutional neural network (CNN) models. These features are further enhanced with climate parameters and classified using the LightGBM algorithm. To further reduce false alarms (FAs), a Constant False Alarm Rate (CFAR) postprocessing step was applied, enhancing the reliability of the detection process. The proposed approach demonstrates exceptional performance, achieving a 99.08% accuracy rate, a false alarm (FAs) rate of only 1%, and an AUC value nearing 1.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".