Hierarchical ship classifier for airborne synthetic aperture radar (SAR) images
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
Lockheed Martin Canada has developed an agent-based adaptable data fusion testbed (ADFT) within the knowledge based system (KBS) architecture which is currently made of a multi-sensor data fusion (MSDF) module and an image support module (ISM). The MSDF module fuses the information provided by nonimaging (2D-radar, ESM) sensors and the various propositions provided by the ISM when processing a synthetic aperture radar (SAR) image. Currently, the ISM processes, simulated and/or real images of ships through a four-step hierarchical classifier that can extract attributes such as ship length, ship category, ship type and ship class. The SAR classifier can distinguish between merchant and combatant categories and can select amongst 5 combatant types. Tests on simulated and real SAR images show a good recognition rate up to the ship type for merchant and line ships.
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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 | 0.001 | 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