Quality Control Assessment of the Mission Airborne Carbon 13 (MAC-13) Hyperspectral Imagery from Costa Rica
Bibliographic record
Abstract
. A data quality assessment of airborne hyperspectral imagery (HSI) from Mission Airborne Carbon 2013 (MAC13) is presented. Because data quality is fundamentally important for modeling landscape biophysical characteristics from HSI, this article presents an assessment related to spectral alignment, spectroradiometric calibration, and geocorrection for 2,700 km2 of imagery acquired with the CASI-1500 and SASI-644 systems (375 nm – 2523 nm, 2.5 m resampled pixel size). MODIS, in-situ and image-based estimations of aerosol optical depth are compared for calculations of visibility for atmospheric correction. Information content (dimensionality) across the 5 ecosystems and 2 developed areas are also compared to illustrate the benefit of the extensive spectral resolution of the data.New approaches to the offset corrections of the imagery improved the accuracy of the calibrated results (radiance and reflectance). Assessment of visibility values applied to the atmospheric correction adduced that apparent reflectance computed using in-scene modeled visibility produced the most similar results to ground spectra. Dimensionality analysis revealed increased information content for all ecosystems when both sensors were considered. While not every HSI issue can be completely compensated for, an appreciation of common artifacts allows users to make more informed decision about their impact on planned analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".