Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry
Bibliographic record
Abstract
Abstract. This research quantifies and discusses atmospheric effects, which alter the radiance observed by a ground-based thermal-infrared (TIR) camera. The TIR camera is mounted on a boom at a height of 125 m above ground on top of a high-rise building in the city of Berlin, Germany (52.4556° N, 13.3200° E) and observes the Earth's surface. The study shows that atmospheric correction of TIR imagery of the three-dimensional (3-D) urban environment acquired in oblique viewing geometry has to account for spatial variability of line-of-sight (LOS) geometry. We present an atmospheric correction procedure that uses these spatially distributed LOS geometry parameters, the radiative transfer model MODTRANTM5.2 and atmospheric profile data derived from meteorological measurements in the field of view (FOV) of the TIR camera. The magnitude of atmospheric effects varies during the analysed 24-hourly period (6 August 2009) and is particularly noticeable for surfaces showing a strong surface-to-air temperature difference. The differences between uncorrected and corrected TIR imagery reach up to 6.7 K at 12:00. The use of non-spatially distributed LOS parameters leads to errors of up to 3.7 K at 12:00 and up to 0.5 K at 24:00.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".