A new passive polarimetric imaging system collecting polarization signatures in the visible and infrared bands
Bibliographic record
Abstract
Electro-optical imaging systems are frequently employed during surveillance operations and search and rescue missions to detect various targets of interest in both the civilian and military communities. By incorporating the polarization of light as supplementary information to such electro-optical imaging systems, it may be possible to increase the target discrimination performance considering that man-made objects are known to depolarize light in different manners than natural backgrounds. Consequently, many passive Stokes-vector imagers have been developed over the years. These sensors generally operate using one single spectral band at a time, which limits considerably the polarization information collected across a scene over a predefined specific spectral range. In order to improve the understanding of the phenomena that arise in polarimetric signatures of man-made targets, a new passive polarimetric imaging system was developed at Defence Research and Development Canada - Valcartier to collect polarization signatures over an extended spectral coverage. The Visible Infrared Passive Spectral Polarimetric Imager for Contrast Enhancement (VIP SPICE) operates four broad-band cameras concomitantly in the visible (VIS), the shortwave infrared (SWIR), the midwave infrared (MWIR), and the longwave infrared (LWIR) bands. The sensor is made of four synchronously-rotating polarizers mounted in front of each of the four cameras. Polarimetric signatures of man-made objects were acquired at various polarization angles in the four spectral bands. Preliminary results demonstrate the utility of the sensor to collect significant polarimetric signatures to discriminate man-made objects from their background.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".