From at-sensor observation to at-surface reflectance - calibration steps for earth observation hyperspectral sensors
Bibliographic record
Abstract
With the continued development of space borne hyperspectral sensors (CSA HERO, ESA CHRIS-on-PROBA, ESA SPECTRA, NASA SpectraSat) to follow the EO-1 Hyperion sensor, high spectral and spatial Earth observation data will become more readily available to the research and user communities. With this improvement in spectral and spatial resolution comes the need to have more rigorous image preprocessing. Spectral and spatial registration and radiometric response need to be characterized and applied more frequently, possibly on a scene by scene basis depending on the stability of the sensor. This requires a system that can evaluate a dataset and determine these parameters efficiently and independently. A pre-processing procedure to transform at-sensor signals to at-surface reflectance for Earth Observation hyperspectral imagery has been developed at the Canada Centre for Remote Sensing/Natural Resources Canada (CCRS/NRCan). This process examines an image cube for bad pixels (stripes) and noise levels, determines spectral (smile effect) and spatial (keystone) registration per pixel, as well as evaluating the image cube for optimal signal gain and offset, and applies the relevant corrections. Where applicable, a scene-based (vicarious) calibration procedure can also be applied.
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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 | 0.000 | 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) | 0.000 | 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".