An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land
Bibliographic record
Abstract
An operational scheme for masking cloud-contaminated pixels in Advanced Very High Resolution Radiometer (AVHRR) daytime data over land is developed, evaluated, and presented. Dynamic thresholding is used with channel 1 reflectance data, channel 3 minus channel 4 temperature difference data, and channel 4 minus channel 5 temperature difference data to automatically create a cloud mask for a single image. The dynamic thresholds can be applied in two different ways: to each pixel individually and to classes of pixels determined by an unsupervised minimum Euclidian distance classifier. The dynamic threshold cloud-masking (DTCM) algorithm presented in this study is used to produce cloud masks based on three different configurations: two channels and individual pixels, three channels and individual pixels, and three channels and classes of pixels. These cloud masks are compared with control masks that were created by visual inspection. The results from the clouds from AVHRR (CLAVR) algorithm and the cloud and surface parameter retrieval (CASPR) algorithm are also compared with the control masks. The results of the comparisons indicate that DTCM, applied on a pixel-by-pixel basis, correctly identifies more clear pixels than CASPR or CLAVR while correctly identifying a comparable or higher number of cloud-contaminated pixels.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".