Surehyp: A Python Package To Retrieve Surface Reflectance From Hyperion Imagery
Bibliographic record
Abstract
Hyperion imagery, that has been and still is used for numerous studies, needs to undergo several preprocessing steps, including atmospheric correction, for the bottom of atmosphere reflectance to be retrieved. While multiple algorithms have been presented and used in specific studies, they may not be adapted to the user's needs or easy to implement. In this paper, a publicly available python package (SUREHYP) dedicated to the preprocessing of Hyperion data, implementing previously developed approaches, is presented. Its performances concerning atmospheric correction are compared to those of two other algorithms (FLAASH and QUAC) by examining the differences between predicted reflectances from four radiance images and their associated reflectances as delivered by NASA JPL. The results suggest that the atmospheric correction algorithm of SUREHYP and FLAASH present similar performances and outperform QUAC concerning the reflectance retrieval accuracy. Further work is needed to take topography and adjacency effects into account in SUREHYP.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".