Joint Weighted Schatten- <i>p</i> Norm and Spatial Smoothness Regularization for Hyperspectral and Multispectral Image Fusion With Spectral Variability
Bibliographic record
Abstract
Hyperspectral (HS) and multispectral (MS) images’ fusion aims to improve their spatial resolutions and circumvent the main limitation of HS sensors. However, existing HS–MS fusion methods account for spectral variability fail to consider the global spectral correlation. To overcome this problem, this letter presents a novel joint weighted Schatten-p norm and spatial smoothness regularization for HS–MS fusion account for both spatial and spectral changes. First, the relationship between the spectral variability and the spectral signatures is formulated as an explicit parametric model. Second, to preserve the inherent correlation among the bands, we design a weighted Schatten-p ($ 0\lt p\lt 1 $) norm regularization method, which considers the importance of different components. Third, a spatial smoothness regularization term is exploited to reconstruct the spatial details. Finally, an iterative procedure based on the framework of alternating direction method of multipliers (ADMM) is designed to solve the resulting optimization problem. Extensive experiments on both synthetic and real datasets demonstrate that the proposed method outperforms six state-of-the-art methods from visual and quantitative assessments. The datasets and results are released inhttp://github.com/phan1007/WSGS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".