Processing and Analysis of Polarimetric Ship Signatures from MARSIE: Report on Results for Polar Epsilon
Bibliographic record
Abstract
Abstract : This report presents the initial analysis of a polarimetric synthetic aperture radar (SAR) data set that was acquired during the Oct. 2005 Maritime Sensor Integration Experiment (MARSIE). MARSIE, as part of a larger TTCP activity, was designed to explore the benefits of sensor fusion to solve the target detection and tracking problem. The MARSIE trial was conducted off the East Coast of Canada and brought many sensors to bear on a set of known ship targets that were engaged in a simulated maritime incursion scenario. The Environment Canada CV-580 polarimetric SAR was used as a proxy sensor for RADARSAT-2 polarimetry. MARSIE polarimetry results include observations of ship target radar cross section for co-polarization and cross-polarization channels, the reduction in the probability of missed detection for polarimetric relative to single channel radar operation, and the potential benefit of polarimetric target decomposition to generate ship target classification features and to segment the ship target of interest from the ocean background. A main recommendation of this report is that polarimetry could improve Polar Epsilon (PE) ship detection performance and enhance the PE concept of operations for the surveillance of spatially constrained maritime operational areas of interest such as choke points.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".