Automatic Extraction of Control Points for the Registration of Optical Satellite and LiDAR Images
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Bibliographic record
Abstract
A novel method for automatic extraction of control points for the registration of optical images with Light Detection And Ranging (LiDAR) data is proposed. It is based on transformation-invariant detection of salient image disks (SIDs), which determine the location of control points as the centers of the corresponding image fragments. The SID is described by a feature vector, which, in addition to the coordinates and diameter, includes intensity descriptors and region shape characteristics of the image fragment. SIDs are effectively extracted using multiscale isotropic matched filtering-a visual attention operator that indicates image locations with high-intensity contrast, homogeneity, and local shape saliency. This paper discusses the extraction of control points from both natural landscapes and structured scenes with man-made objects. Registration experiments conducted on QuickBird imagery with corresponding LiDAR data validated the proposed approach.
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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 | 0.000 |
| 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