An image fusion method taking into account phenological analogies and haze
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it