Sub-pixel target detection in LWIR hyperspectral imagery using ground leaving radiance
Bibliographic record
Abstract
The processing chain leading to specific material detection in hyperspectral imagery implies the use of atmospherically corrected images of emissivity or reflectance before comparing image signatures to a database of materials' signatures. This is a sensible approach for the reflective hyperspectral bands l and when the pixels are completely filled with a uniform material in the LWIR bands (8 to 12 microns). In the LWIR, the atmospheric correction process is different of what is used in the reflective bands and involves the use of a temperature and emissivity separation process (TES). If the pixel is not filled with a uniform material and the measured radiance is produced from the mix of materials having different emissivity and temperatures, the output of the TES will not be linear in temperature and in emissivity and will be contaminated by the non-linear mix of the temperature and emissivity of the materials leading to a potential for confusion during the detection process. In this paper, we propose a detection approach using the ground leaving radiance that is used directly to perform detection using emissivity signatures contained in a database. The detection results using this process are compared with the detection results using the output of a TES algorithm. The study is performed in simulation without noise and with the exact knowledge of the downwelling irradiance. The results show that a detection algorithm using the ground leaving radiance performs better than its counterpart using the emissivity when the difference in temperature increases.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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 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".