RPC-Based Coregistration of VHR Imagery for Urban Change Detection
Bibliographic record
Abstract
Abstract In urban change detection, coregistration between bi-temporal Very High Resolution ( vhr ) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration ( pwcr ), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The pwcr method utilizes a Digital Surface Model ( dsm ) and the Rational Polynomial Coefficients ( rpc s) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the pwcr method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the pwcr method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent).
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
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".