Preliminary Comparison of the Multispectral Cameras Onboard UAV Platform for Environment Monitoring
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) provides an alternative way to collect data conveniently, when corresponding sensor (s) is (are) equipped onboard. In this paper, two multispectral cameras were compared mainly based on simulated channel reflectance, which covered visible (i.e., Blue, Green, and Red), near infrared (NIR), and red-edge (RE) regions. The preliminary investigation shows the between-sensor differences in sensor settings, the related differences in channel reflectance, and in the derived vegetation indices. The between-sensor differences in channel reflectance vary with channels. Compared with the Green and NIR channels, the RE and Red channels show more obvious between-sensor difference, which contributes significantly to the differences in the derived vegetation indices. The between-sensor differences in the RE channel are required to be eliminated, because the RE reflectance and the derived indices correspondingly are important in modeling biophysical parameters. Case study shows that the between-sensor differences of vegetation indices were generally more obvious than the reflectance differences of individual channels. Damage assessment for plant over flooded areas was readily done with the help of vegetation indices. Accordingly, the between-sensor differences are likely significant in practical applications. In conclusion, the importance to make consistency among data collections by different sensors (i.e., onboard UAV) is highlighted, and an effective way for between-sensor transformation is required in further investigations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".