An image fusion method taking into account phenological analogies and haze
Bibliographic record
Abstract
In the phenology of pixels, as the end-member proportions of a pixel vary with the progression of seasons, the pixel changes through different versions. In the image fusion of a low spatial resolution multiresolution (MS) pixel, multiple high spatial resolution fused pixels are generated. The original MS pixel and each fused pixel superimpose over different panchromatic (PAN) pixels and have different end-member proportions. Since analogies exist between pixel phenology and image fusion, the spectral change directions of pixels in phenology can be used as a reference to obtain optimal spectral change directions for MS sub-pixels in image fusion. Regarding pixel phenology, it was found that the optimal spectral change direction for an MS sub-pixel in image fusion is along the sub-pixel vector minus a haze vector. Based on this direction and a multivariate regression between the MS and PAN images, we propose a new method for image fusion. In an evaluation using spatially degraded IKONOS MS and PAN images, this method outperforms some selected current image fusion methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".