Performance of the FIRST: a long-wave infrared hyperspectral imaging sensor
Bibliographic record
Abstract
Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state are fundamental needs in several fields of applications. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on FTIR technology to provide high spectral resolution and to enable high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320x256 pixels at 0.35 mrad spatial resolution over the 8-12 μm spectral range at spectral resolutions of up to 0.25 cm-1. The FIRST has been used in several field measurements, including demonstration of standoff chemical agent detection. One key feature of the FIRST is its ability to give calibrated measurements. The quality of the calibrated measurements will be presented in this paper. Sensitivity, spectral resolution and radiometric stability as obtained during field and laboratory measurements will be presented. Finally, images of chemical releases detected with the FIRST will be shown.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".