Robust hyperspectral signal unmixing in the presence of correlated noise
Bibliographic record
Abstract
Hyperspectral imaging analysis aims at the estimation of the number of constituent substances, known as endmembers, their spectral signatures as well as their abundance fractions . Due to the nature of hyperspectral sensors, output data is mostly associated with correlated noise rather than with the white Gaussian noise considered in most of the analysis. In the presence of correlated noise, estimation of dimensionality with the assumption of white noise is associated with consider able error. This error in the very first step will be propagated to the next steps and fully invalidate the unmixing process. On the other hand, existing methods which consider a correlated noise are lacking in robustness to noise. A Whitened Noiseless Code-length method (WNCLM) is presented for hyperspectral signals dimension estimation and unmixing in the presence of spectrally or spatially correlated noise. Variance and correlation coefficients are calculated to estimate the noise correlation matrix. This matrix is further used to whiten the noise. New processed hyperspectral data then goes through a simultaneous denoising and Least Square Error (LSE) based unmixing process that leads to the estimation of data dimensionality. Some numerical simulations are provided to illustrate the effectiveness of our proposed method.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.008 | 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 teacher head, 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".