Impact of spectral curvature on at-surface reflectance accuracy and information extraction techniques
Bibliographic record
Abstract
In Canada's arctic, the landscape is a complex mixture of exposed rock with varying levels of lichen and tundra vegetation, illuminated by a low elevation sun. Imaging spectrometry (hyperspectral remote sensing) has been shown useful for mapping northern environments, with applications in geological exploration and habitat mapping. Use of hyperspectral imagery aims to exploit spectral characteristics unique to surface cover classes. This assumes pixels provide a linear combination of spectral reflectance from existing sub-pixel components, referred to as end-members. The level of success can be directly impacted by data quality. Insuring the highest quality data is being used requires a robust pre-processing system which evaluates known artefacts, such as spectral curvature (spectral smile), noise, and spectral gain error. In many systems, spectral smile is often the most overlooked source of uncertainty. To evaluate one aspect of hyperspectral imagery for northern environments, the impact of spectral smile is explored with relation to spectral indices which are used to discriminate between surface material types.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".