Retrieval of Surface Reflectance from Hyperion Radiance Data
Bibliographic record
Abstract
Surface Reflectance was retrieved from Hyperion data by a procedure involving data preprocessing steps and atmospheric correction. Several steps are required to remove sensor artifacts in the spectral and spatial domain. These artifacts include a spatial shift in the short-wave infrared (SWIR) data, an along-track striping, a spatial misalignment of the visible near infrared (VNIR) and SWIR data, and a cross-track spectral (smile/frown) effect. A look-up table approach in combination with the radiance transfer code MODTRAN4.2 was applied to preprocessed at-sensor radiance data to retrieve surface reflectance. Results indicate that the spectral calibration data provided do not achieve a proper positioning of the bands' centre wavelengths. Wavelength shifts of up to 1 nm in the VNIR and up to 3 nm in the SWIR remain. This can be seen especially at wavelengths affected by strong gaseous absorptions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".