Targets, methods, and sites for assessing the in-flight spatial resolution of electro-optical data products
Bibliographic record
Abstract
The spatial resolution of a digital, electro-optical remote sensing imaging system or product is an important image quality characteristic that helps determine the utility of an imaging source. Although spatial resolution is often described by a single image quality parameter, the ground sample distance, there are several other parameters that affect image sharpness and need to be considered. These other parameters are associated with the point-spread function, signal-to-noise ratio, and dynamic range of the image product. This review paper covers the various approaches to in-flight measurement of spatial resolution parameters, including ground sample distance, point spread function, optical transfer function, modulation transfer function, far field response, and edge response and their significance, as well as target types and methods to determine these spatial resolution parameters. To this end, the paper lists and describes various targets found across the world, as well as astronomical ones. These targets are appropriate for evaluating a wide range of image scale products. For high spatial resolution imaging systems, the types of targets range from engineered fixed and deployable targets to agricultural and urban features, allowing almost any site to be used for determining spatial resolution. Independent, comprehensive image product evaluation sites that are currently in use in the US and Europe are also described.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".