Accuracy assessment of hyperspectral imagery: atmospheric calibration and image classification considerations
Bibliographic record
Abstract
Accuracy assessment is one of the most important considerations in the evaluation of remotely sensed imagery. Too often, it is not done when imagery is produced. The accuracy of an image is effected by many variables, including the spatial and spectral resolution of the hyperspectral sensor, processing statistics used, types of classifications chosen, limits of detection of different surface materials, suitability of reference spectra used for image analysis training, the type and amount of ground truth data acquisition, and type of atmospheric correction algorithm applied to the imagery. This presentation will discuss selected examples generated from work performed under the NASA EOCAP (Earth Observations Commercial Applications Program) project NAS 13-99004. The first example is from the Ray copper mine in Arizona, USA. It demonstrates the affects of spectral library references vs in situ ground truth, and different processing techniques on the identification and distribution of a target mineral, jarosite, in an image. The second example shows how the choice of processing cutoffs can change the distribution of a target mineral, alunite, in the image. The third example evaluates old and new atmospheric correction algorithms.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".