High resolution multispectral photogrammetric imagery: enhancement, interpretation and evaluations
Bibliographic record
Abstract
A variety of aerial mapping cameras were adapted and developed into simulated multiband digital photogrammetric mapping systems. Direct digital multispectral, two multiband cameras (IIS 4 band and Itek 9 band) and paired mapping and reconnaissance cameras were evaluated for digital spectral performance and photogrammetric mapping accuracy in an aquatic environment. Aerial films (24cm X 24cm format) tested were: Agfa color negative and extended red (visible and near infrared) panchromatic, and; Kodak color infrared and B&W (visible and near infrared) infrared. All films were negative processed to published standards and digitally converted at either 16 (color) or 10 (B&W) microns. Excellent precision in the digital conversions was obtained with scanning errors of less than one micron. Radiometric data conversion was undertaken using linear density conversion and centered 8 bit histogram exposure. This resulted in multiple 8 bit spectral image bands that were unaltered (not radiometrically enhanced) "optical count" conversions of film density. This provided the best film density conversion to a digital product while retaining the original film density characteristics. Data covering water depth, water quality, surface roughness, and bottom substrate were acquired using different measurement techniques as well as different techniques to locate sampling points on the imagery. Despite extensive efforts to obtain accurate ground truth data location errors, measurement errors, and variations in the correlation between water depth and remotely sensed signal persisted. These errors must be considered endemic and may not be removed through even the most elaborate sampling set up. Results indicate that multispectral photogrammetric systems offer improved feature mapping capability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".