Image Prediction Using Coordinated Hyperspectral and RGB Video of Dynamic Natural Water Scenes
Bibliographic record
Abstract
A bimodal video imaging platform combining RGB and 371-band hyperspectral imaging systems was used to collect time-series data of the Lake Ontario shoreline at Hamlin Beach State Park in Rochester, New York, USA. We predicted the hyperspectral image frames of dynamic natural water scenes at previous and later points in time using a paired relationship between the time-series hyperspectral imagery and RGB video. The time-series hyperspectral image data was collected using our Headwall Hyperspec micro-HE line-scanning imaging spectrometer integrated into a General Dynamics pan-tilt unit. RGB video data was collected with a low-cost consumer GoPro Hero 8 Black. We detail our data collection methods and characterize the predictions using distributions of absolute and normalized residuals in reflectance spaces. Within visible wavelengths, 95% of the scene is predicted to within 2% absolute reflectance. The normalized error percentage of these residuals translates to approximately 30% of signal level for water spectra. In the near-infrared regime, the normalized error percentage of the residuals sharply increases to approximately 90% for 95% of the scene due to lack of band information from the RGB video imagery of our shallow water scene.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".