Methods to Calibrate a Digital Colour Camera as a Multispectral Imaging Sensor in Low Light Conditions
Bibliographic record
Abstract
High-sensitivity multispectral imaging sensors for scientific use are expensive and consequently not available to scientific teams with limited financial resources. Such sensors are used in applications such as nighttime remote sensing, astronomy, and night time studies in general. In this paper, we present a method aiming to transform non-scientific multispectral imaging sensors into science-friendly ones. The method consists in developing a calibration procedure applied to digital colour cameras not initially designed for scientific purposes. One of our targets for this project was that the procedure would not require any complex or costly equipment. The development of this project was motivated by a need to analyze airborne and spaceborne pictures of the earth surface at night, as a way to determine the optical properties (e.g., light flux, spectrum type and angular emission function) of artificial light sources. This kind of information is an essential part of the input data for radiative transfer models used to simulate light pollution and its effect on the natural environment. Examples of applications of the calibration method are given for that specific field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".