<title>An ad-hoc approach for quality assessment of hyperspectral datacubes in target detection</title>
Bibliographic record
Abstract
This paper addresses assessment of different processing techniques for hyperspectral images target detection. An ad-hoc quality assessment approach is adopted to compare different noise reduction techniques of hyperspectral images for target detection applications. Two different noise reduction techniques are applied to a datacube collected over a well-studied area with human made targets. The quality of these noise reduced datacubes in preserving the identity of the targets of interest is compared with that of the original datacube. This is achieved by applying different measures on the datacubes. First, the Virtual Dimensionality is used and the results for both of the noise reduction methods are compared with those of the original datacube for several false-alarm probabilities. Then Maximum Noise Fraction is applied to the datacubes and its capability in finding a transform in which the information of the datacube is represented in a smaller number of bands is assessed. Finally using set measures and knowing the location of the targets, different classes are defined and the intraclass and interclass distances for each datacube is measured.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".