METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS
Bibliographic record
Abstract
This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance Difference (VD), Standard Deviation Difference (SDD), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), Relative Dimensionless Global Error (ERGAS), and Q4 Quality Index (Q4), which were also used in the IEEE GRSS 2006 Data Fusion Contest. Four testing images are generated to evaluate the indexes. Visual comparison and digital classification demonstrate that the four testing images have the same quality for remote sensing applications; however, the seven evaluation methods provide different measurements indicating that the four images have varying qualities. The image fusion quality evaluation by Alparone, et al. (2004) and that by the IEEE GRSS 2006 data fusion contest (Alparone, et al., 2007) are also analyzed. Significant discrepancy between the quantitative measurements, visual comparison and final ranking has been found in both evaluations. The inconsistency between the visual evaluations and quantitative analyses in the above three cases demonstrate that the seven quantitative indicators cannot provide reliable measurements for quality assessment of remote sensing images.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".