<title>Web-based automatic multisensor image registration using the CEONet</title>
Bibliographic record
Abstract
In this paper we present a new Web-based application for registering multi-sensor satellite images for image fusion operations. It is a distributed processing system which offers automatic or semi-automatic image registration and it is intended to provide a service to the Canadian Geospatial Data Infrastructure (CGDI) users through the GeoConnections Discovery Portal, formerly CEONet. It will be also provided on the web page of A.U.G. Signals Ltd.(www.augsignals.com) which will be connected to CEONet and CGDI. This innovative technology of A.U.G. Signals has all the advantages of current registration techniques, plus is can estimate reference (control) points automatically at high degree of accuracy and with zero false alarms. Users who apply existing remote sensing software tools, such as PCI or IDL/ENVI, with geo-referenced points for registration, may employ the A.U.G. Signals software to further improve the registration accuracy of their images. Geo-referenced control points may also be used with the proposed software. The proposed service is expected to evolve and expand other distributed processing initiatives of current interest, such as the emerging GRID technologies under development in United States and Europe and the Canadian high-speed network CA*Net3 and be part of the US OGC Web based Initiative.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.030 |
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".