Integration of off-nadir VHR imagery with elevation data for advanced information extraction
Bibliographic record
Abstract
Integration or registration of optical images with elevation data is important for many applications such as building detection. Despite the fact that VHR (very high resolution) satellite images are acquired mostly off-nadir, the use of such images in the previous research works is less common than the nadir ones. This is due to the high relief distortion of above-ground objects, such as buildings, in these off-nadir images. For the applications of information extraction, which prefer the integration of the optical images and elevation data, this relief distortion makes the integration between the perspective off-nadir VHR optical images and orthographic digital surface models (DSMs) almost impossible unless a true orthorectification process is implemented. However, true orthoimages are expensive, time consuming and mostly difficult to achieve. This paper proposes an integration method of the DSMs elevations with off-nadir VHR optical imagery by projecting the ground elevations back to the image space using the relevant sensor model. Elevation data is derived using off-nadir VHR stereo images which one of them is then used for the integration. The proposed method is found to be efficient in terms of sub-pixel accuracy and ease of implementation. Additionally, it preserves the original image information which is essential for some applications such as image classification.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".