Accuracy assessment of hyperspectral imagery: atmospheric calibration and image classification considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it