A comparison of Backscatter Intensity of Icebergs in C- and L-band SAR Imagery
Bibliographic record
Abstract
Images from satellite Synthetic Aperture Radar (SAR) systems are widely used for iceberg monitoring. Normally, icebergs are detected in SAR images by utilizing constant false alarm rate (CFAR) filters, which compare each pixel or cluster of pixels against its background and adaptively set a threshold based on several assumptions regarding the statistical distribution of the background clutter. CFAR algorithms are currently being applied on images from the C-band SAR Sentinel-1 and RADARSAT missions by the operational ice services responsible for Canadian and Greenland waters. Previous studies have shown that imagery from wide-swath C-band SAR is unsuitable for detecting icebergs surrounded by sea ice, but other studies have indicated that icebergs in sea ice may be detected in high-resolution L-band SAR images. Additionally, it is well known that L-band SAR is less sensitive to sea surface roughness than C-band SAR. Therefore, a future operational L-band SAR mission is currently being investigated by the European Space Agency (ESA) since it is expected that L-band images are valuable complements to current C-band imagery for iceberg detection in areas with drift ice and in rough windy seas. In this project, we investigate the backscatter intensity contrast between icebergs and their surroundings using ALOS-2 PALSAR-2 (L-band) ScanSAR, and Sentinel-1 (C-band) extra wide swath imagery. The investigations are concentrated on SAR images from two test sites, one in the Labrador Sea, where we – for further analysis - identified 256 icebergs in open water, and another site in the region of Belgica Bank with 1013 icebergs embedded in fast ice. The investigation shows that the two SAR sensors performed similarly for the open water site, with a backscatter intensity contrast between icebergs and the background of 5-6 dB in both the HH and HV band. But for icebergs surrounded by sea ice, the contrast between icebergs and background at both C- and L-band is greatly reduced to around 2 dB for the HH channel and 4-5 dB for the HV channel. By further manually classifying the sea ice types around the icebergs, we show that the backscatter contrast between icebergs and background is similar at C- and L-band for icebergs embedded in smooth sea ice. However, for rough sea ice, the C-band contrast is decreasing, while remaining high at L-band. Our results indicate that L-band data will lead to better performance for detecting icebergs surrounded by sea ice.
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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 | 0.000 | 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".