Narrow band SWIR hyperspectral imaging: a new approach based on volume Bragg grating
Bibliographic record
Abstract
Volume Bragg grating technology has enabled the development of a new type of staring hyperspectral camera. Based on Bragg Tunable filters, these hyperspectral cameras have both high spectral and spatial resolution, and significantly higher sensitivity than competing technologies like push broom spectrometer, liquid crystal tunable filters, or acousto-optic tunable filters. They are minimally sensitive to polarization and their spectral isolation can reach 106. Here we thus present an innovative tool to collect SWIR hyperspectral data with high spectral and spatial resolution. This new instrument is based on a 3nm bandwidth Bragg Tunable Filter, continuously tunable from 1.0um and 2.5um. Because high spectral resolution also means less light per channel, a low noise custom HgCdTe (MCT) camera was also developed to meet the requirement of the filter. The high speed capability of more than 300 fps and the low operating temperature of 200K (deep cooled option to 77K) allow full frame 500 spectral channel datacube acquisitions in minimal time. Basic principle of this imaging filter will be reviewed as well as the custom MCT camera performances. High resolution hyperspectral measurements will be demonstrated between 1.0um and 2.5um on different objects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".