Multi-Scale and Multi-Stream Fusion Network for Pansharpening
Why this work is in the frame
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Bibliographic record
Abstract
Pansharpening refers to the use of a panchromatic image to improve the spatial resolution of a multi-spectral image while preserving spectral signatures. However, existing pansharpening methods are still unsatisfactory at balancing the trade-off between spatial enhancement and spectral fidelity. In this paper, a multi-scale and multi-stream fusion network (named MMFN) that leverages the multi-scale information of the source images is proposed. The proposed architecture is simple, yet effective, and can fully extract various spatial/spectral features at different levels. A multi-stage reconstruction loss was adopted to recover the pansharpened images in each multi-stream fusion block, which facilitates and stabilizes the training process. The qualitative and quantitative assessment on three real remote sensing datasets (i.e., QuickBird, Pléiades, and WorldView-2) demonstrates that the proposed approach outperforms state-of-the-art 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 | 0.000 |
| 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