Orthorectification and Pan-Sharpening of WorldView-2 Satellite Imagery to Produce High Resolution Coloured Ortho-Photos
Bibliographic record
Abstract
In the last decade VHR (Very High Resolution) images from satellite, because of the reduced dimensions of pixel (less than 1 meter) and the availability in different acquisition bands (4 or more), have had major dffusion in many application fields of remote sensing. They can be used also to produce high resolution coloured ortho-photos, but adequate levels of positional accuracy as well as small pixel dimensions are necessary. The aim of this paper is to demonstrate that WorldView-2 (WV-2) images satisfy totally these requirements if firstly submitted to high accurate rectification and Pan-Sharpening processes. Using Rational Polynomial Functions (RPFs), original dataset can be better overlapped to cartographic maps at medium or great scale; multispectral images (cell size: 2 m) can be resampled to meet geometric resolution of pan one (cell size: 0.5 m), so detailed and attendible RGB composition results. Applications are carried out on one sample of WV-2 imagery concerning a scene within the Province of Caserta (Italy) that includes vegetated as well as urban areas. Finally RGB composition with pixel dimensions of 0.5 m, positional accuracy less than 1 meter and likely colors are achieved, confirming the possibility to use this type of images for coloured ortho-photos at scale 1:5.000 at least.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.002 |
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".