The applicability of radiative transfer models for atmospherically correcting airborne hyperspectral data in Antarctica
Bibliographic record
Abstract
As part of a collaborative project between BAS, DRDC Suffield (Canada) and ITRES Research Ltd., (Canada) the first known airborne hyperspectral dataset was acquired over the Antarctic in February 2011. The simultaneous deployment of commercially available visible-near infrared and shortwave infrared spectrometers generated a dataset covering 0.35 to 2.5 µm spectral range at a spectral resolution of 9.6-14 nm. To enable quantitative analysis of surface properties using imaging spectrometry data the removal of atmospheric absorption and scattering effects is an essential pre-processing step. The implementation of a sufficiently accurate and robust atmospheric correction methodology is of critical importance in ensuring that the results from spectral and spatial analysis algorithms are as accurate as possible. However, whilst methodologies are well established for most environments, there is currently no published methodology for correcting airborne hyperspectral data in the Antarctic region. This study presents initial results from an investigation into the applicability of the MODTRAN-5® radiative transfer model and the ATCOR-4 atmospheric correction package for producing atmospherically corrected hyperspectral data in the unique Antarctic environment; an environment that is cold, dry and has low levels of aerosols and atmospheric pollution. Initial results from radiative transfer modelling and atmospheric correction produce absolute reflectance spectra which are partially comparable to laboratory measured spectra. Improvements are seen with the hybrid approach of radiative transfer modelling and the empirical line method using in-scene ground targets. Residual noise remains present due to absorption by atmospheric gases and aerosols which are not appropriately modelled for this environment. Overall, this demonstrates that commercially available packages are not currently flexible enough to correct Antarctic hyperspectral data without the addition of in-scene ground calibration targets. The implementation of Antarctic aerosol and atmospheric profiles into the radiative transfer model would likely improve these corrections and remains an area of investigation for future hyperspectral campaigns in the region.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".