Detection and correction of abnormal pixels in Hyperion images
Bibliographic record
Abstract
Hyperion images are currently processed to level 1a (from level 0 or raw data). These level 1a images are files of radiometrically corrected data in units of either watts/(sr /spl times/ micron /spl times/ m/sup 2/) /spl times/ 40 for VNIR bands or watts/(sr /spl times/ micron /spl times/ m/sup 2/) /spl times/ 80 for SWIR bands. Each distributed Hyperion level 1a image tape contains a log file, called "(EO-1 identifier).fix.log", that reports the bad or corrupted pixels (called known bad pixels) found during the pre-flight checking, and details how they were fixed. All bad pixels should be corrected in a level 1a image. However, bad pixels are still evident. In addition, there are dark vertical stripes in the image that are not reported in the log file. In this paper, we introduce a method to detect and correct the bad pixels and vertical stripes (we will refer to these occurrences as abnormal pixels). Images from the Greater Victoria Watershed and other EVEOSD test sites are used to determine how stationary the locations of the abnormal pixels are. After abnormal pixel correction a Hyperion image is ready for geometric correction, atmospheric correction, and further 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".