Information theoretic assessment of correlated noise in hyperspectral signal unmixing
Why this work is in the frame
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Bibliographic record
Abstract
Hyperspectral imaging sensors simultaneously acquire data in hundreds of spectral bands, facilitating detailed study of a scanned object. Unmixing the hyperspectral data as well as estimating the intrinsic dimension of hypercube requires an accurate evaluation of the noise structure. Existing methods mostly simplify the evaluation by considering a white Gaussian noise. However, due to the nature of the hyperspectral sensors,the noise is highly correlated in spectral dimension leading to an inaccurate estimation for white noise assumption. In this paper, we firstly evaluate the strength of the correlation in adjacent spectral bands. Evaluation results prove that only adjacent bands exhibit a significant correlation. Based on the results, we have proposed an explanatory model for the noise structure to extract the correlation coefficients and second order statistics of noise in spectral bands. Simulation results show that our proposed Hyperspectral Correlation Extractor (HYCE) method is accurately estimating the noise structure and is robust to the variation of noise statistics. Our method that is specifically proposed for hyperspectral imaging applications shows unmixing results with an accurate estimation of the pure materials (endmembers) and the related mapping.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it