High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data
Why this work is in the frame
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Bibliographic record
Abstract
Due to the difference of “mean information” between substitution component and substituted component, spectral distortion often occurs in component substitution (CS) pansharpening. In this paper, a data fitting scheme is adopted to improve spectral quality in image fusion based on well-established CS approach. A generalized CS framework that is capable of modeling any CS image fusion method is also presented. In this framework, instead of injecting detail information of panchromatic (Pan) image into substituted component, the data fitting strategy is designed to adjust the mean information of Pan image in the construction of substitution component. The data fitting scheme involves two matrix subtractions and one matrix convolution. It is fast in implementation and is effective to avoid the spectral distortion problem. Experimental results on a large number of Pan and multispectral images show that the improved CS methods have good performance on the spatial and spectral fidelity. Moreover, experiments carried out on large-size images also show an excellent running time performance of the proposed 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 | 0.001 | 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 | 0.000 |
| 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