Mapping lithology in Canada's Arctic: application of hyperspectral data using the minimum noise fraction transformation and matched filtering
Bibliographic record
Abstract
A test site in southern Baffin Island, Canada has been established to study the applications of hyperspectral data to lithological mapping. Good bedrock exposure and minimal vegetation cover provide an ideal environment for the evaluation of hyperspectral remote sensing. Airborne PROBE hyperspectral data were collected over the study site during the summer of 2000. Processing methods involved (1) applying a minimum noise fraction (MNF) transformation to the data and visual interpretation of a ternary colour MNF image to produce a lithologicalcompositional map, and (2) selection of end members from the MNF image followed by matched filtering based on the selected end members to produce a lithologicalcompositional map. Both methods have produced a lithological map that compares favourably with the existing geological map. Although lichen imparts a similarity to the spectra throughout the visible and near infrared and short-wave infrared ranges, this study has shown that enough variability in the spectra as a function of different mineralogy was present to successfully discriminate one major lithological group (metatonalites) and three compositional units (psammites, quartzites, and monzogranites). Vegetation could be clearly distinguished, which in this area only is a good proxy for mapping metagabbroic rocks. Furthermore, discrimination of slightly different compositional units within the psammites and the metatonalites was also possible. The results from this study indicate that hyperspectral remotely sensed imagery holds promise for lithological mapping in Canada's North, although further analysis is required in different geologic environments in Canada's North to validate hyperspectral remote sensing as a useful aid to litho logical mapping.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".