High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".