Radiometric correction of satellite imagery for topographic and atmospheric effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it