Bibliographic record
Abstract
Current surveillance and reconnaissance systems require improved capability to enable the co-registration of larger images, combining enhanced temporal, spatial, and spectral resolutions. However, such proficient remote sensing systems cannot employ traditional manual exploitation techniques to cope successfully with the avalanche of data to be processed and analyzed. Automated image exploitation tools may be employed if the images are already co-registered together. Therefore, there is a need to develop fully automated co-registration algorithms able to deal with different scenarios, and helpful to be used successively for numerous applications such as image data fusion, change detection, and target detection. This paper describes the Automated Multi-sensor Image Registration (AMIR) system and embedded algorithms under development at DRDC-Valcartier. The AMIR system provides a framework for the automated multi-date registration of electro-optic images, acquired from different sensors and from dissimilar oblique view angles. The system is characterized by its fully automated nature, where no user intervention prevailed. Advanced image algorithms are used in order to supply the capability to register multi-date electro-optic images acquired from different viewpoints, under singular operational conditions, multiple scenarios (e.g. airport, harbor, vegetation, urban, etc.), different spatial resolutions (e.g. IKONOS/QuickBird, Airborne/Spaceborne), while providing sub-pixel accuracy registration level.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".