Estimation of Mineral Abundance From Hyperspectral Data Using a New Supervised Neighbor-Band Ratio Unmixing Approach
Bibliographic record
Abstract
This article compares the ability of nine unmixing models, including radiative transfer (RT) models as well as a new nonlinear unmixing approach called neighbor-band ratio unmixing (NBRU), to obtain mineralogical information from hyperspectral data. Their performance in estimating mineral abundances of 94 crafted mineral mixtures was first assessed. NBRU led to the best results among non-RT models with mean and median errors of 9.8% and 7.4%, respectively. Hapke's and Shkuratov's RT models obtained 6.5% and 5.6%, and 6.7% and 4.7%, respectively. In a second experiment, the mapping ability of six non-RT models and their robustness when facing endmember variability were evaluated. The assessment was performed on an AVIRIS hyperspectral image of the widely studied Cuprite area, NV, USA. Comparisons with validation maps showed that NBRU retrieved the best spatial distributions for seven of the nine minerals mapped.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".