S-Space: A new concept for information extraction from imaging spectrometer data
Bibliographic record
Abstract
Imaging spectroscopy records the solar reflected spectrum at a fine spectral resolution and in a large number of bands thereby producing a spectral profile associated with each pixel in an image. This type of data tends to be highly correlated and we intend to harness the information of this spectral dependence by introducing the S-space concept. This concept in conjunction with measures of spatial dependence allows one to visualize the spectral profile as a regionalized variable where distance is measured in wavelengths. Unlike image space, S-space is one-dimensional. We illustrate the S-space concept using a CASI image of a forest scene and an AVIRIS image of an urban scene. This new technique provides spectral correlation information for each individual spectral profile on a per-pixel basis rather than the spectral variability across the entire image as is traditionally done in remote sensing investigations. As an example of the possibilities, spectral dependence was quantified using the semivariogram in S-space. A model of spatial dependence was then fitted to each semivariogram and the model parameters used as input to a classification algorithm in order to extract land cover information. To compare our approach with standard techniques, we used the first three principal components to produce a land cover classification. The semivariogram model parameter derived classification results displayed a better spatial contiguity and greatly diminished the dimensionality of the dataset. We also discuss future directions for the use of the S-space concept.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".