Enhanced target detection and identification using multispectral and hyperspectral polarimetric thermal measurements
Bibliographic record
Abstract
The performance of thermal electro-optic infrared sensors (EO/IR) may be limited in certain specific circumstances, particularly for the detection and identification of targets embedded in an isothermal scene, i.e. when there is insufficient thermal contrast between the targets against their surrounding background. Such situations generally occur at the beginning and end of the day, but can also happen at any time during the day. One way to cope with this limitation is to employ EO/IR sensors that are sensitive to the polarization states of light. With this intention, Defence Research and Development Canada (DRDC) has developed thermal infrared multispectral and hyperspectral polarimetric imaging systems and spectral algorithms to extract the polarized radiance components of targets of interest, and use this additional information to enhance detection and identification while reducing false alarm rate. This paper presents experimental results from measurements using ground-based multispectral and hyperspectral polarimetric imaging sensors to acquire the polarized radiance of targets set up at multiple orientation angles with respect to the sensors lineof-sight (LOS). The objectives of the experiments were to study the phenomenology of polarized surface radiance in the Long-Wave Infrared (LWIR) and assess the effect of different materials on the resulting s-polarized and p-polarized spectral components. Experimental results show the advantages of thermal multispectral and hyperspectral polarimetric imaging sensors over conventional unpolarized ones to discriminate targets against their background, particularly during thermal cross-over periods.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".