Sensitivity of Spectral Unmixing Analysis to a Spectrally Dependent Gain Error in Hyperspectral Data
Bibliographic record
Abstract
In support of phase a work on the proposed hyperspectral environment and resource observer (HERO) mission, the sensitivity of the results obtained from a common hyperspectral analysis technique, linear spectral unmixing, to a spectrally dependent error in the radiometric calibration of the hyperspectral data is investigated. Two ground-based mineral reflectance spectra are selected as spectral endmembers and combined linearly to give five different mixtures. The MODTRAN atmospheric correction model, as implemented in the imaging spectrometer data analysis system (ISDAS), is used to convert these ground-based reflectance spectra to top-of- atmosphere (TOA) radiance. The resulting mixed spectra are then subjected to a randomly generated spectral gain error (SGE). This is repeated a statistically significant number of times to produce a simulated dataset for each of the five mineral combinations. By varying the magnitude of the introduced SGE, several simulated datasets are produced representing different levels of relative calibration accuracies in the spectral domain. The simulated TOA data sets are then converted back to ground-based reflectance, once again using MODTRAN. Linear constrained spectral unmixing is then applied to each of the simulated datasets. Each of the original mixtures results in a distribution of fractions, the width of which is dependent on the magnitude of the SGE applied to the data set providing the relationship between the unmixing error and the SGE. A specification of the acceptable error in the unmixing results then dictates the required level of accuracy in the spectral gain calibration. The relationship between the SGE and the error in the unmixing results is shown to be directly proportional.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".