Experiences Learned in the Acquisition, Processing, and Assessment of In-situ Point Spectroscopy Measurements Supporting Airborne Hyperspectral Cal/Val activities
Bibliographic record
Abstract
The process of acquiring and processing in-situ point spectroscopy measurements as ‘ground-truth’ reflectance is often viewed as straightforward and uncomplicated. In reality, the process requires significant attention to detail. This is particularly true as it applies to its use in activities related to the calibration and validation of airborne and/or satellite hyper/multi-spectral imagery where unbiased traceable results are crucial. In this presentation, I review methodologies employed by the airborne hyperspectral remote sensing group at the National Research Council Canada to optimize the acquisition of in-situ point spectroscopy measurements and the processing of target reflectance spectra as performed in support of a bottomup (lab field airborne satellite (Sentinel-2)) data end-product validation project. In addition, methodologies to assess the quality of the resulting reflectance spectra are discussed. The laboratory portion of the approach was designed to provide an initial reference panel reflectance characterization in terms of the biconical reflectance factor (BCRF) followed by regular monitoring of panel degradation. Making use of a laboratory implementation of a SVC 1024i field spectrometer, the BCRF of field reflectance reference panels were cross-calibrated at a 0°:45° view/illumination geometry against our primary lab reference panel. This lab panel had, in turn, been calibrated by the Remote Sensing Group at the University of Arizona tying our results to the NIST reflectance standard. Assessment of these data sets, acquired under controlled laboratory conditions, identified potential artifacts related to the detector temperature and integration times in the SVC 1024i field spectrometer. We see many examples where these effects have gone unnoticed within datasets acquired in less aware field deployments. Experiences related to the acquisition of robust field spectrometry measurements are then reviewed along with methods we apply to evaluate the quality and suitability of the resultant datasets given the less than ideal atmospheric conditions commonly encountered. Biases due to inconsistent location of inscattering objects, reference panel leveling, solar angle procession, and variances in downwelling illumination conditions are also considered.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".