Radiometric correction of satellite imagery for topographic and atmospheric effects
Bibliographic record
Abstract
The radiometry of satellite imagery is influenced by ground cover, local topography, and atmosphere. In order to increase the accuracy of ground cover identification from satellite imagery, effects due to topography and atmosphere must be removed. These effects can be estimated by modeling the image-formation process. For this thesis an image-formation model is developed and tested on Landsat MSS data over a mountainous region. Solar illumination angle, atmosphere depth, and sky illumination are calculated with the help of a digital elevation model. A digital forest cover map is used to select a target forest type for which model parameters are estimated using regression analysis. Results of this analysis indicate that solar illumination angle has the largest effect on target pixel irradiance followed by atmosphere depth. Sky illumination as calculated, was significantly correlated with target pixel irradiance but in a negative sense. This correlation suggests that inter reflection (also called mutual illumination) from adjacent terrain may be a small but significant source of illumination. The estimated model parameters are used to correct the imagery for topographic and atmospheric effects. Visual assessment of the corrected imagery indicates that many but not all of the topographic effects have been reduced. Comparisons between computer classified imagery and the forest cover map show an improvement in correctly classified pixels from 54% for the original image to 72% for the corrected image.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".