Spectral angle mapper based assessment of detectability of man-made targets from hyperspectral imagery after SNR enhancement
Why this work is in the frame
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Bibliographic record
Abstract
This paper assesses the effectiveness of a signal-to-noise ratio (SNR) enhancement technology for hyperspectral imagery to examine whether it can better serve remote sensing applications. A hyperspectral data set acquired using an airborne Short-wave-infrared Full Spectrum Image II with man-made targets in the scene of the data set was tested. Spectral angle mapper and end-members of different target materials were used to measure the superficies of the targets and to assess the detectability of the targets before and after applying the SNR enhancement technology to the data set. The experimental results show that small targets, which cannot be detected in the original data set due to inadequate SNR and low spatial resolution, can be detected after the SNR of the data set is enhanced.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it